Method and System for Providing Enhanced Location Based Service (ELBS) Trilateration using a Single Device

ABSTRACT

Methods, devices and systems for generating enhanced location information on or about a mobile device may include receiving (via a processor of the mobile device) location information from one or more of a sensor of the mobile device and an external system, generating (via the processor) a first waypoint based on the received location information, receiving network provided location information, generating a second waypoint based on the received network provided location information, retrieving dead reckoning location information, generating a third waypoint based on the received network provided location information, applying the first, second and third waypoints to a kalman filter to generate precise location information, and using the precise location information to provide the enhanced location based service.

RELATED APPLICATIONS

This application is a continuation in part of U.S. patent application Ser. No. 14/834,758 entitled “Method and System for Providing Enhanced Location Based Information for Wireless Handsets” filed on Aug. 25, 2015, which is a continuation of U.S. patent application Ser. No. 14/690,713 entitled “Method and System for Providing Enhanced Location Based Information for Wireless Handsets” filed on Apr. 20, 2015, which is a continuation in part of U.S. patent application Ser. No. 14/293,056 entitled “Method and System for Providing Enhanced Location Based Information for Wireless Handsets” filed on Jun. 2, 2014, which is a continuation of U.S. patent application Ser. No. 13/585,125 entitled “Method and System for Providing Enhanced Location Based Information for Wireless Handsets” filed Aug. 14, 2012, and which claims the benefit of priority of U.S. Provisional Application No. 61/575,300, entitled “Method and System for Providing Enhanced Location Based Information for Wireless Handsets” filed Aug. 18, 2011, and U.S. Provisional Application No. 61/573,636, entitled “Method and System for Providing Enhanced Location Based Information for Wireless Handsets” filed Sep. 9, 2011, the entire contents of all of which are hereby incorporated by reference.

FIELD OF INVENTION

The present application relates generally to a wireless mobile communication system, and more particularly to methods and systems that provide enhanced location information for wireless mobile devices.

BACKGROUND

Wireless communication technologies and mobile electronic devices (e.g., cellular phones, tablets, laptops, etc.) have grown in popularity and use over the past several years. To keep pace with increased consumer demands, mobile electronic devices have become more powerful and feature rich, and now commonly include global positioning system (GPS) receivers, sensors, and many other components for connecting users to friends, work, leisure activities and entertainment. However, despite these advancements, mobile devices remain lacking in their ability to provide effective location based services, information, or communications. As mobile devices and technologies continue to grow in popularity and use, generating enhanced location information for mobile devices is expected to become an important and challenging design criterion for mobile device manufactures and network engineers.

SUMMARY

The methods and apparatuses of various embodiments provide devices, circuits and methods for precise location determination in multi-technology communications devices. Embodiment methods may include determining, by a processor of the mobile device, an initial position; generating, by the processor, at least one set of local position information based on locally determined location information; receiving, by an antenna of the mobile device, location information from one or more external location tracking systems; generating, by the processor, at least one set of external position information based on the received location information from the one or more external location tracking systems; receiving, by the antenna, distance information from multiple mobile devices in a communication group; generating, by the processor, at least one set of proximity position information based on the received location information from the multiple mobile devices in the communication group; generating, by the processor a final location estimation set comprising a position, velocity, and acceleration, based on the set of local position information, the set of external position information, the set of proximity position information and the initial position; and utilizing, by the processor, the final location estimation set to provide a location based service.

In some embodiments, the position, velocity and acceleration each have a latitude component, a longitude component, and an altitude component. In such embodiments, generating any of the set of local position information, the set of external position information, or the set of proximity position information may further include separating, by the processor, location information into a set of latitude components, a set of longitude components, and a set of altitude components; and executing, by the processor, a kalman filter on each of the set of latitude components, the set of longitude components, and the set of altitude components to produce position information. Such embodiments may include, determining an error associated with generation of the position information.

Some embodiments may include, updating, by the processor, the set of local position information after a predetermined time interval to produce updated local position information; updating, by the processor, the set of external position information after a predetermined time interval to produce updated external position information; and updating, by the processor, the set of proximity position information after a predetermined time interval to produce updated proximity position information. Such embodiments may include updating, by the processor, the final location estimation set based on the updated set of local position information, the updated set of external position information, the updated set of proximity position information and the final location estimation set to produce an updated final location estimation set. Alternatively, in such embodiments, updating any of the local position information, external position information, or proximity position information may comprise: determining, by the processor, if updated location information or distance information is available; combining, by the processor, current location information or distance information with an error value, in response to determining that updated location information or updated distance information is not available. In another alternative of such embodiments, the predetermined interval may be a time interval between successive determinations of the locally determined location information.

In some embodiments, the distance information may comprise a distance from the mobile device to a respective wireless communications device.

In some embodiments, the locally determined location information may be obtained using dead reckoning

In some embodiments, locally determined location information may be obtained using combination of local sensor outputs.

In some embodiments, the one or more external tracking systems may comprise a global positioning (GPS).

Further embodiments include methods of providing an enhanced location based service via a mobile device, including receiving (via a processor of the mobile device) location information from one or more of a sensor of the mobile device and an external system, generating (via the processor) a first waypoint based on the received location information, receiving network provided location information, generating a second waypoint based on the received network provided location information, retrieving dead reckoning location information, generating a third waypoint based on the received network provided location information, applying the first, second and third waypoints to a kalman filter to generate precise location information, and using the precise location information to provide the enhanced location based service.

In an embodiment, receiving the location information from one or more of a sensor of the mobile device and an external system includes receiving one or more of global positioning system (GPS) data, Loran C data, Cellular identifier (Cell ID) data, WiFi data, beacon data, and radio frequency identification (RFID) data, and receiving the network provided location information includes receiving the network provided location information from one of a network server and an eNodeB. In an embodiment, the method may include sending the precise location information to a network server, receiving on the mobile device location information from the network server in response to sending the precise location information, and generating more precise location information based on the location information received from the network server.

In a further embodiment, sending the precise location information to a network server includes sending one of a final waypoint and a final location estimation set to the network server, and generating more precise location information based on the location information received from the network server includes generating one of an updated final waypoint and an updated final location estimation set based on the location information received from the network server. In an embodiment, the method may include grouping the mobile device with a wireless transceiver in proximity to the mobile device to form a communication group, sending the precise location information to the wireless transceiver, receiving on the mobile device location information from the wireless transceiver in response to sending the precise location information, and generating more precise location information based on the location information received from the wireless transceiver.

In an embodiment, the method may include determining whether the location information from one or more of the sensor and the external system has been reported, and generating the first waypoint based on the received location information includes generating a new waypoint and selecting the new waypoint as the first waypoint in response to determining that the location information from one or more of the sensor and the external system has been reported, and selecting as the first waypoint one of a plurality of previously generated waypoints in response determining that the location information from one or more of the sensor and the external system has not been reported.

In a further embodiment, generating the first waypoint based on the received location information includes further includes determining whether a memory of the mobile device stores previously generated waypoints, and generating the first waypoint to include a null value in response to determining that the memory does not store previously generated waypoints, and in which selecting as the first waypoint one of a plurality of previously generated waypoints in response determining that the location information from one or more of the sensor and the external system has not been reported includes selecting as the first waypoint one of the plurality of previously generated waypoints in response determining that memory of the mobile device stores previously generated waypoints.

In an embodiment, the method may include determining whether the network provided location information has been reported, and generating the second waypoint based on the network provided location information includes generating a new waypoint and selecting the new waypoint as the second waypoint in response to determining that the network provided location information has been reported, and selecting as the second waypoint one of a plurality of previously generated waypoints in response determining that the network provided location information has not been reported.

In an embodiment, the method may include determining whether the dead reckoning location information is available, and determining whether the dead reckoning location information has been reported in response to determining that the dead reckoning location information is available, and generating the third waypoint based on the dead reckoning location information includes generating the third waypoint to include a null value in response to determining that dead reckoning location information is available, generating a new waypoint and selecting the new waypoint as the third waypoint in response to determining that the dead reckoning location information has been reported, and selecting as the third waypoint one of a plurality of previously generated waypoints in response determining that the dead reckoning location information has not been reported.

In a further embodiment, generating a third waypoint based on the received network provided location information includes generating a location estimation set that includes a position value, a velocity value and an acceleration value based on the network provided location information. In a further embodiment, generating a third waypoint based on the received network provided location information includes generating a location estimation set that includes latitude value, a longitude value, and an altitude value based on the network provided location information. In a further embodiment, applying the first, second and third waypoints to the kalman filter to generate precise location information includes applying the first, second and third waypoints to the kalman filter to generate a final location estimation set including position information, velocity information, and acceleration information. In a further embodiment, the position information, velocity information, and acceleration information each include a latitude value, a longitude value, and an altitude value.

Further embodiments include a non-transitory computer readable storage medium having stored thereon processor-executable software instructions configured to cause a processor of a mobile device to perform operations including receiving location information from one or more of a sensor of the mobile device and an external system, generating a first waypoint based on the received location information, receiving network provided location information, generating a second waypoint based on the received network provided location information, retrieving dead reckoning location information, generating a third waypoint based on the received network provided location information, applying the first, second and third waypoints to a kalman filter to generate precise location information, and using the precise location information to provide the enhanced location based service.

In an embodiment, the stored processor-executable software instructions may be configured to cause a processor of a mobile device to perform operations such that receiving the location information from one or more of a sensor of the mobile device and an external system includes receiving one or more of global positioning system (GPS) data, Loran C data, Cellular identifier (Cell ID) data, WiFi data, beacon data, and radio frequency identification (RFID) data, and receiving the network provided location information includes receiving the network provided location information from one of a network server and an eNodeB.

In a further embodiment, the stored processor-executable software instructions may be configured to cause a processor of a mobile device to perform operations further including sending the precise location information to a network server, receiving on the mobile device location information from the network server in response to sending the precise location information, and generating more precise location information based on the location information received from the network server. In a further embodiment, the stored processor-executable software instructions may be configured to cause a processor of a mobile device to perform operations such that sending the precise location information to a network server includes sending one of a final waypoint and a final location estimation set to the network server, and generating more precise location information based on the location information received from the network server includes generating one of an updated final waypoint and an updated final location estimation set based on the location information received from the network server.

In a further embodiment, the stored processor-executable software instructions may be configured to cause a processor of a mobile device to perform operations further including grouping the mobile device with a wireless transceiver in proximity to the mobile device to form a communication group, sending the precise location information to the wireless transceiver, receiving on the mobile device location information from the wireless transceiver in response to sending the precise location information, and generating more precise location information based on the location information received from the wireless transceiver.

In a further embodiment, the stored processor-executable software instructions may be configured to cause a processor of a mobile device to perform operations further including determining whether the location information from one or more of the sensor and the external system has been reported, and the stored processor-executable software instructions may be configured to cause a processor of a mobile device to perform operations such that generating the first waypoint based on the received location information includes generating a new waypoint and selecting the new waypoint as the first waypoint in response to determining that the location information from one or more of the sensor and the external system has been reported, and selecting as the first waypoint one of a plurality of previously generated waypoints in response determining that the location information from one or more of the sensor and the external system has not been reported.

In a further embodiment, the stored processor-executable software instructions may be configured to cause a processor of a mobile device to perform operations such that generating the first waypoint based on the received location information includes further includes determining whether a memory of the mobile device stores previously generated waypoints, and generating the first waypoint to include a null value in response to determining that the memory does not store previously generated waypoints, and the stored processor-executable software instructions may be configured to cause a processor of a mobile device to perform operations such that selecting as the first waypoint one of a plurality of previously generated waypoints in response determining that the location information from one or more of the sensor and the external system has not been reported includes selecting as the first waypoint one of the plurality of previously generated waypoints in response determining that memory of the mobile device stores previously generated waypoints.

In a further embodiment, the stored processor-executable software instructions may be configured to cause a processor of a mobile device to perform operations further including determining whether the network provided location information has been reported, and the stored processor-executable software instructions may be configured to cause a processor of a mobile device to perform operations such that generating the second waypoint based on the network provided location information includes generating a new waypoint and selecting the new waypoint as the second waypoint in response to determining that the network provided location information has been reported, and selecting as the second waypoint one of a plurality of previously generated waypoints in response determining that the network provided location information has not been reported.

In a further embodiment, the stored processor-executable software instructions may be configured to cause a processor of a mobile device to perform operations further including determining whether the dead reckoning location information is available, and determining whether the dead reckoning location information has been reported in response to determining that the dead reckoning location information is available, and the stored processor-executable software instructions may be configured to cause a processor of a mobile device to perform operations such that generating the third waypoint based on the dead reckoning location information includes generating the third waypoint to include a null value in response to determining that dead reckoning location information is available, generating a new waypoint and selecting the new waypoint as the third waypoint in response to determining that the dead reckoning location information has been reported, and selecting as the third waypoint one of a plurality of previously generated waypoints in response determining that the dead reckoning location information has not been reported.

In a further embodiment, the stored processor-executable software instructions may be configured to cause a processor of a mobile device to perform operations such that generating a third waypoint based on the received network provided location information includes generating a location estimation set that includes a position value, a velocity value and an acceleration value based on the network provided location information. In a further embodiment, the stored processor-executable software instructions may be configured to cause a processor of a mobile device to perform operations such that generating a third waypoint based on the received network provided location information includes generating a location estimation set that includes latitude value, a longitude value, and an altitude value based on the network provided location information.

In a further embodiment, the stored processor-executable software instructions may be configured to cause a processor of a mobile device to perform operations such that applying the first, second and third waypoints to the kalman filter to generate precise location information includes applying the first, second and third waypoints to the kalman filter to generate a final location estimation set including position information, velocity information, and acceleration information. In a further embodiment, the stored processor-executable software instructions may be configured to cause a processor of a mobile device to perform operations such that the position information, velocity information, and acceleration information each include a latitude value, a longitude value, and an altitude value.

Further embodiments include a mobile device, including a memory, and a processor coupled to the memory, and the processor is configured with processor-executable instructions to perform operations including receiving location information from one or more of a sensor of the mobile device and an external system, generating a first waypoint based on the received location information, receiving network provided location information, generating a second waypoint based on the received network provided location information, retrieving dead reckoning location information, generating a third waypoint based on the received network provided location information, applying the first, second and third waypoints to a kalman filter to generate precise location information, and using the precise location information to provide the enhanced location based service.

In an embodiment, the processor may be configured with processor-executable instructions to perform operations such that receiving the location information from one or more of a sensor of the mobile device and an external system includes receiving one or more of global positioning system (GPS) data, Loran C data, Cellular identifier (Cell ID) data, WiFi data, beacon data, and radio frequency identification (RFID) data, and receiving the network provided location information includes receiving the network provided location information from one of a network server and an eNodeB.

In a further embodiment, the processor may be configured with processor-executable instructions to perform operations further including sending the precise location information to a network server, receiving on the mobile device location information from the network server in response to sending the precise location information, and generating more precise location information based on the location information received from the network server. In a further embodiment, the processor may be configured with processor-executable instructions to perform operations such that sending the precise location information to a network server includes sending one of a final waypoint and a final location estimation set to the network server, and generating more precise location information based on the location information received from the network server includes generating one of an updated final waypoint and an updated final location estimation set based on the location information received from the network server.

In a further embodiment, the processor may be configured with processor-executable instructions to perform operations further including grouping the mobile device with a wireless transceiver in proximity to the mobile device to form a communication group, sending the precise location information to the wireless transceiver, receiving on the mobile device location information from the wireless transceiver in response to sending the precise location information, and generating more precise location information based on the location information received from the wireless transceiver.

In a further embodiment, the processor may be configured with processor-executable instructions to perform operations further including determining whether the location information from one or more of the sensor and the external system has been reported, and the processor may be configured with processor-executable instructions to perform operations such that generating the first waypoint based on the received location information includes generating a new waypoint and selecting the new waypoint as the first waypoint in response to determining that the location information from one or more of the sensor and the external system has been reported, and selecting as the first waypoint one of a plurality of previously generated waypoints in response determining that the location information from one or more of the sensor and the external system has not been reported. In a further embodiment, the processor may be configured with processor-executable instructions to perform operations such that generating the first waypoint based on the received location information includes further includes determining whether a memory of the mobile device stores previously generated waypoints, and generating the first waypoint to include a null value in response to determining that the memory does not store previously generated waypoints, and the processor may be configured with processor-executable instructions to perform operations such that selecting as the first waypoint one of a plurality of previously generated waypoints in response determining that the location information from one or more of the sensor and the external system has not been reported includes selecting as the first waypoint one of the plurality of previously generated waypoints in response determining that memory of the mobile device stores previously generated waypoints.

In a further embodiment, the processor may be configured with processor-executable instructions to perform operations further including determining whether the network provided location information has been reported, and the processor may be configured with processor-executable instructions to perform operations such that generating the second waypoint based on the network provided location information includes generating a new waypoint and selecting the new waypoint as the second waypoint in response to determining that the network provided location information has been reported, and selecting as the second waypoint one of a plurality of previously generated waypoints in response determining that the network provided location information has not been reported.

In a further embodiment, the processor may be configured with processor-executable instructions to perform operations further including determining whether the dead reckoning location information is available, and determining whether the dead reckoning location information has been reported in response to determining that the dead reckoning location information is available, and the processor may be configured with processor-executable instructions to perform operations such that generating the third waypoint based on the dead reckoning location information includes generating the third waypoint to include a null value in response to determining that dead reckoning location information is available, generating a new waypoint and selecting the new waypoint as the third waypoint in response to determining that the dead reckoning location information has been reported, and selecting as the third waypoint one of a plurality of previously generated waypoints in response determining that the dead reckoning location information has not been reported.

In a further embodiment, the processor may be configured with processor-executable instructions to perform operations such that generating a third waypoint based on the received network provided location information includes generating a location estimation set that includes a position value, a velocity value and an acceleration value based on the network provided location information. In a further embodiment, the processor may be configured with processor-executable instructions to perform operations such that generating a third waypoint based on the received network provided location information includes generating a location estimation set that includes latitude value, a longitude value, and an altitude value based on the network provided location information.

In a further embodiment, the processor may be configured with processor-executable instructions to perform operations such that applying the first, second and third waypoints to the kalman filter to generate precise location information includes applying the first, second and third waypoints to the kalman filter to generate a final location estimation set including position information, velocity information, and acceleration information. In a further embodiment, the processor may be configured with processor-executable instructions to perform operations such that the position information, velocity information, and acceleration information each include a latitude value, a longitude value, and an altitude value.

Embodiments include a multi-technology communication device having one or more processors or processor cores configured with processor-executable instructions to perform operations of one or more of the embodiment methods described above.

Embodiments include a non-transitory processor-readable medium having stored thereon processor-executable software instructions to cause a processor to perform operations of one or more of the embodiment methods described above or herein.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are incorporated herein and constitute part of this specification, illustrate exemplary embodiments of the invention, and, together with the general description given above and the detailed description given below, serve to explain features of the invention.

FIG. 1 is a communication system block diagram illustrating network components of an example telecommunication system suitable for use in a mobile-device centric approach for determining the location of a mobile device in accordance with various embodiments.

FIG. 2 is a communication system block diagram illustrating network components of an example telecommunication system suitable for use in a network centric approach for determining the location of a mobile device in accordance with various embodiments.

FIG. 3 is an illustration of an example mobile device suitable for use in grouping with other mobile devices and computing precise location information in accordance with the various embodiments.

FIG. 4A is a communication system block diagram illustrating network components of an example LTE communication system suitable for use with various embodiments

FIG. 4B is a block diagram illustrating logical components, communication links and information flows in an embodiment communication system.

FIGS. 5A-5C are component block diagrams illustrating functional components, communication links, and information flows in an embodiment method of grouping mobile devices and sharing location information between grouped mobile devices.

FIG. 5D is a process flow diagram illustrating an embodiment mobile device method for grouping mobile devices and sharing location information between grouped mobile devices and the network to compute enhanced location information.

FIGS. 6A-6D are component block diagrams illustrating functional components, communication links, and information flows in an embodiment method for computing location information in which the grouped/paired mobile devices are updated with their respective location information.

FIG. 6E is a process flow diagram illustrating an embodiment system method of determining the location of two or more grouped mobile devices.

FIG. 6F is a process flow diagram illustrating an embodiment mobile device method of adjusting the update intervals in response to detecting a low battery condition.

FIG. 7 is a component block diagram illustrating functional components, communication links, and information flows in embodiment method of periodically scan for cells.

FIG. 8 is a process flow diagram illustrating an embodiment mobile device method for determining the location of a mobile device in a wireless network.

FIGS. 9A-9E are component block diagrams illustrating various logical and functional components, information flows and data suitable for use in various embodiments.

FIG. 10 is a sequence diagram illustrating an embodiment hybrid lateration method by which mobile devices may calculate position accurately with the help of the network.

FIG. 11 is a sequence diagram illustrating another embodiment hybrid lateration method in which a mobile device cannot locate a network due coverage problems.

FIGS. 12A-12C are component block diagrams illustrating functional components, communication links, and information flows in an embodiment method of transferring a connection from a local radio system to the small cell system.

FIGS. 13A-13C are component block diagrams illustrating functional components, communication links, and information flows in an embodiment method of identifying and responding to a distressed mobile device.

FIG. 14 is a component block diagrams illustrating functional components, communication links, and information flows in an embodiment method of performing dead reckoning grouping mobile devices in an ad-hoc scheme.

FIG. 15 is a process flow diagram illustrating an embodiment mobile device method for determining the location of a mobile device.

FIGS. 16A-B are process flow diagrams illustrating an embodiment methods of obtaining position information for a mobile device in a wireless network.

FIG. 17 is a process flow diagram illustrating an embodiment method of updating location information in the absence of new location measurements.

FIG. 18 is a process flow diagram illustrating an embodiment method to produce precise location information.

FIG. 19 is a component block diagram of a mobile device suitable for use with an embodiment.

FIG. 20 is a component block diagram of a server suitable for use with an embodiment.

FIG. 21A is a block diagram illustrating various components, information flows, and operations in an example mobile device system that is configured to perform enhanced location based service (eLBS) trilateration operations in accordance with various embodiments.

FIG. 21B is a block diagram illustrating various components, information flows, and operations in an example mobile device system that is configured to perform single device eLBS trilateration operations in accordance with various embodiments

FIG. 21C is a block diagram illustrating various components, information flows, and operations in a device/system configured to perform eLBS trilateration operations in accordance with some embodiments.

FIG. 22 is a process flow diagram illustrating a method of performing eLBS trilateration operations in accordance with an embodiment.

FIGS. 23 through 30 are a process flow diagrams that illustrate method(s) of performing eLBS operations in accordance with various other embodiments.

FIG. 31 is a block diagram illustrating various components in a mobile device that is configured so that sensor inputs are under the control of the operating system and used by an eLBS module configured to perform eLBS operations in accordance with an embodiment.

FIG. 32 is a block diagram illustrating various components in a mobile device that is equipped with a sensor hub and includes an eLBS module configured to perform eLBS operations in accordance with another embodiment.

FIG. 33 is a call flow diagram illustrating information flows and operations in a system configured to receive and use network based location information to perform the eLBS trilateration operations in accordance with the various embodiments.

FIG. 34 is a process flow diagram illustrating a method of using network based location information to perform the eLBS trilateration operations in accordance with an various embodiment.

DETAILED DESCRIPTION

The various embodiments will be described in detail with reference to the accompanying drawings. Wherever possible, the same reference numbers will be used throughout the drawings to refer to the same or like parts. References made to particular examples and implementations are for illustrative purposes, and are not intended to limit the scope of the invention or the claims.

The word “exemplary” is used herein to mean “serving as an example, instance, or illustration.” Any implementation described herein as “exemplary” is not necessarily to be construed as preferred or advantageous over other implementations.

The terms “mobile device,” “cellular telephone,” and “cell phone” are used interchangeably herein to refer to any one or all of cellular telephones, smartphones, personal data assistants (PDA's), laptop computers, tablet computers, ultrabooks, palm-top computers, wireless electronic mail receivers, multimedia Internet enabled cellular telephones, wireless gaming controllers, and similar personal electronic devices which include a programmable processor, a memory and circuitry for sending and/or receiving wireless communication signals. While the various embodiments are particularly useful in mobile devices, such as cellular telephones, which have limited battery life, the embodiments are generally useful in any computing device that may be used to wirelessly communicate information.

The terms “wireless network”, “network”, “cellular System”, “cell tower” and “radio access point” may used generically and interchangeably to refer to any one of various wireless mobile systems. In an embodiment, wireless network may be a radio access point (e.g., a cell tower), which provides the radio link to the mobile device so that the mobile device can communicate with the core network.

A number of different cellular and mobile communication services and standards are available or contemplated in the future, all of which may implement and benefit from the various embodiments. Such services and standards include, e.g., third generation partnership project (3GPP), long term evolution (LTE) systems, third generation wireless mobile communication technology (3G), fourth generation wireless mobile communication technology (4G), global system for mobile communications (GSM), universal mobile telecommunications system (UMTS), 3GSM, general packet radio service (GPRS), code division multiple access (CDMA) systems (e.g., cdmaOne, CDMA2000™), enhanced data rates for GSM evolution (EDGE), advanced mobile phone system (AMPS), digital AMPS (IS-136/TDMA), evolution-data optimized (EV-DO), digital enhanced cordless telecommunications (DECT), Worldwide Interoperability for Microwave Access (WiMAX), wireless local area network (WLAN), public switched telephone network (PSTN), Wi-Fi Protected Access I & II (WPA, WPA2), Bluetooth®, integrated digital enhanced network (iden), and land mobile radio (LMR). Each of these technologies involves, for example, the transmission and reception of voice, data, signaling and/or content messages. It should be understood that any references to terminology and/or technical details related to an individual telecommunication standard or technology are for illustrative purposes only, and are not intended to limit the scope of the claims to a particular communication system or technology unless specifically recited in the claim language.

A number of different methods, technologies, solutions, and/or techniques (herein collectively “solutions”) are currently available for determining the location of mobile device, any or all of which may be implemented by, included in, and/or used by the various embodiments. Such solutions include, e.g., global positioning system (GPS) based solutions, assisted GPS (A-GPS) solutions, and cell-based positioning solutions such as cell of origin (COO), time of arrival (TOA), observed time difference of arrival (OTDOA), advanced forward link trilateration (AFLT), and angle of arrival (AOA). In various embodiments, such solutions may implemented in conjunction with one or more wireless communication technologies and/or networks, including wireless wide area networks (WWANs), wireless local area networks (WLANs), wireless personal area networks (WPANs), and other similar networks or technologies. By way of example, a WWAN may be a Code Division Multiple Access (CDMA) network, a Frequency Division Multiple Access (FDMA) network, an OFDMA network, a 3GPP LTE network, a WiMAX (IEEE 802.16) network, and so on. The WPAN may be a Bluetooth network, an IEEE 802.15x network, and so on. A WLAN may be an IEEE 802.11x network, and so on. A CDMA network may implement one or more radio access technologies (RATs) such as CDMA2000, Wideband-CDMA (W-CDMA), and so on.

Various embodiments discussed herein may generate, compute, and/or make use of location information pertaining to one or more mobile devices. Such location information may be useful for providing and/or implementing a variety of location-based services, including emergency location services, commercial location services, internal location services, and lawful intercept location services. By way of example: emergency location services may include services relating to the provision of location and/or identification information to emergency service personal and/or emergency systems (e.g., to 911 system); commercial location services may include any general or value-added service (e.g., asset tracking services, navigation services, location-based advertising services, etc); internal location services may include services pertaining to the management of the wireless service provider network (e.g., radio resource management services, message delivery services, paging services, call delivery services, services for providing position/location network enhancements, etc.); and lawful intercept location services may include any service that provides public safety and/or law enforcement agencies with identification and/or location information pertaining to a mobile device or a mobile device user. While the various embodiments are particularly useful in applications that fall within one or more of the categories/types of location based services discussed above, the embodiments are generally useful in any application or service that benefits from location information.

Modern mobile electronic devices (e.g., mobile phones) typically include one or more geospatial positioning systems/components for determining the geographic location of the mobile device. Location information obtained by these geospatial systems may be used by location-aware mobile software applications (e.g., Google® Maps, Yelp®, Twitter® Places, “Find my Friends” on Apple®, etc.) to provide users with information regarding the mobile device's physical location at a given point in time. In recent years, such location-based services and software applications have increased in popularity and use, and now enable mobile device users to navigate cities, read reviews of nearby restaurants and services, track assets or friends, obtain location-based safety advice, and/or take advantage of many other location-based services on their mobile devices.

Consumers of modern mobile devices now demand more advanced, robust, and feature-rich location-based services than that which is currently available on their mobile devices. However, despite many recent advances in mobile and wireless technologies, mobile devices remain lacking in their ability to provide their users/consumers with location based services that are accurate or powerful enough to meet the demands of these consumers. For example, while existing location-aware mobile software applications (e.g., “Find my Friends” on Apple®, Google® Latitude, etc.) enable a mobile device user to view the approximate geographical position of other mobile devices on a two-dimensional map, they lack the capability to accurately, efficiently and consistently pin point the precise location and/or position of the other mobile devices in all three dimensions and/or within a wireless communication network. The various embodiments overcome these and other limitations of existing solutions by collecting information from multiple mobile devices, generating more precise location information on or about one or more mobile devices, generating advanced three-dimensional location and position information on or about one or more mobile devices, and using the generated location information to provide mobile device users with more accurate, more powerful, and more reliable location based services.

One of the challenges associated with using geo-spatial positioning technology on a mobile device is that the mobile device's ability to acquire satellite signals and navigation data to calculate its geospatial location (called “performing a fix”) may be hindered when the mobile device is indoors, below grade, and/or when the satellites are obstructed (e.g., by tall buildings, etc.). The presence of physical obstacles, such as metal beams or walls, may cause multipath interference and signal degradation of the wireless communication signals when the mobile device is indoors or in urban environments that include tall buildings or skyscrapers. In rural environments, the mobile device may not have sufficient access to satellite communications (e.g., to a global positioning system satellite) to effectively ascertain the mobile device's current location. These and other factors often cause existing geo-spatial technologies to function inaccurately and/or inconsistently on mobile devices, and hinder the mobile device user's ability to fully utilize location-aware mobile software applications and/or other location based services and applications on his/her mobile device.

Another problem with using existing geo-spatial positioning technologies is that position accuracy afforded by existing technologies is not sufficient for use in emergency services due to the relatively high level of position accuracy required by these services.

The various embodiments include improved location determination solutions that determine the location of a mobile device at the level of position accuracy which is suitable for use in emergency location services, commercial location services, internal location services, and lawful intercept location services.

Generally, there are three basic approaches for determining the location of mobile devices in a communication network: a mobile-device centric approach, a network centric approach and a hybrid approach that may include aspects of both the mobile device centric approach and the network centric approach.

FIG. 1 illustrates an example communication system 100 suitable for implementing a mobile-device centric approach for determining the location of a mobile device 102 in accordance with various embodiments. The mobile device 102 may include a global positioning system (GPS) receiver in communication with multiple geo-spatial positioning and navigation satellites 110 and a base tower 104 of a communication network 106. The mobile device 102 may receive (e.g., via the GPS receiver) radio signals emitted by the satellites 110, measure the time required for the signals to reach the mobile device 102, and use trilateration techniques to determine the geographical coordinates (e.g., latitude and longitude coordinates) of the mobile device 102. The mobile device 102 may send the geographical coordinates to the communication network 106 at various times and/or in response to various conditions or events, such as upon initial acquisition with the communication network 106, in response to network-based requests, in response to third party requests, etc.

In an embodiment, the communication network may be a cellular telephone network. A typical cellular telephone network includes a plurality of cellular base stations 104 coupled to a network operations center 108, which operates to connect voice and data calls between mobile devices 102 (e.g., mobile phones) and other network destinations, such as via telephone land lines (e.g., a POTS network, not shown) and the Internet 114. Communications between the mobile devices 102 and the cellular telephone network 11 may be accomplished via two-way wireless communication links, such as 4G, 3G, CDMA, TDMA, and other cellular telephone communication technologies. The network 106 may also include one or more servers 112 coupled to or within the network operations center 108 that provide connections to the Internet 114.

In various embodiments, the mobile device 102 may be configured to communicate with a radio access node, which can include any wireless base station or radio access point such as LTE, CDMA2000/EVDO, WCDMA/HSPA, IS-136, GSM, WiMax, WiFi, AMPS, DECT, TD-SCDMA, or TD-CDMA and switch, Land Mobile Radio (LMR) interoperability equipment, a satellite Fixed Service Satellite (FSS) for remote interconnection to the Internet and PSTN.

FIG. 2 illustrates an example communication system 200 suitable for implementing a network centric approach for determining the location of a mobile device 102 in accordance with various embodiments. The mobile device 102 may include a circuitry for wirelessly sending and receiving radio signals. The communication system 200 may include a plurality of radio access points 204, 206 having installed thereon additional radio equipment 208 for measuring the location of the mobile devices in the communication system. For example, the mobile device 102 may transmit radio signals for reception by one or more (e.g., typically three) radio access points 204, and the radio access points may receive the transmitted signals and measure the signal strength and/or radio energy of the received signals to identify the location of the mobile device 102.

In an embodiment, the radio access points 204 may be configured to determine the location of the mobile device relative to a known location of a network component, such as the illustrated radio access point 206. In this manner, the additional radio equipment 208 installed on the radio access points 204, 206 provides the communication system 200 with similar functionality as is provided by a GPS receiver for signals received from the mobile device. For example, the radio equipment on one or more of the radio access points 204 may measure how long it takes for the radio signal to travel from the mobile device 102 to another radio access point 206, and using trilateration techniques (e.g., time of arrival, angle of arrival, or a combination thereof), the mobile device 102 or a network server 210 may estimate the location of the mobile device 102 to within an accuracy of 100 to 300 meters. Once the network has estimated the latitude and longitude coordinates of the mobile device 102, this information may be used to determine the geo-spatial location of the mobile device 102, which may be communicated to other systems, servers or components via the Internet 114.

Various embodiments may implement and/or make use of a hybrid approach for determining the location of mobile devices in a communication network, which may include aspects of both the device-centric and the network-centric approaches discussed above with reference to FIGS. 1 and 2. For example, an embodiment system, mobile device or network component (e.g., severs, radio access points, etc.) may be configured to implement a hybrid approach in which dead reckoning (also known as “deduced reckoning”) techniques, GPS capabilities of the mobile device, and mobile-to-mobile (i.e., mobile device to mobile device) trilateration are used to produce position estimates of increased accuracy. In another embodiment, the system, devices and/or components may be configured to implement a hybrid approach in which the GPS capabilities of mobile devices, the measured signal strengths and/or radio energy of radio signals transmitted from the mobile devices, and known locations of network components may be used in combination to estimate the locations of one or more mobile devices in the network. In a further embodiment, system, devices and/or components may be configured to dynamically determine the factors (e.g., radio signal strength, GPS, etc.) to measure and/or use in determining the location of the mobile devices.

FIG. 3 illustrates sample components of a mobile device 102 in the form of a phone that may be used with the various embodiments. The phone may include a speaker 304, user input elements 306, microphones 308, an antenna 312 for sending and receiving electromagnetic radiation, an electronic display 314, a processor 324, a memory 326 and other well known components of modern electronic devices.

The phone may also include one or more sensors 310 for monitoring physical conditions (e.g., location, motion, acceleration, orientation, altitude, etc.). The sensors may include any or all of a gyroscope, an accelerometer, a magnetometer, a magnetic compass, an altimeter, an odometer, and a pressure sensor. The sensors may also include various bio-sensors (e.g., heart rate monitor, body temperature sensor, carbon sensor, oxygen sensor, etc.) for collecting information pertaining to environment and/or user conditions. The sensors may also be external to the mobile device and paired or grouped to the mobile device via a wired or wireless connection (e.g., Bluetooth®, etc.). In embodiment, the mobile device 102 may include two or more of the same type of sensor (e.g., two accelerometers, etc.).

The phone may also include a GPS receiver 318 configured to receive GPS signals from GPS satellites to determine the geographic location of the phone. The phone may also include circuitry 320 for transmitting wireless signals to radio access points and/or other network components. The phone may further include other components/sensors 322 for determining the geographic position/location of the phone, such as components for determining the radio signal delays (e.g., with respect to cell-phone towers and/or cell sites), performing trilateration and/or multilateration operations, identifying proximity to known networks (e.g., Bluetooth® networks, WLAN networks, WiFi, etc.), and/or for implementing other known geographic location technologies.

The phone may also include a system acquisition function configured to access and use information contained in a subscriber identity module (SIM), universal subscriber identity module (USIM), and/or preferred roaming list (PRL) to, for example, determine the order in which listed frequencies or channels will be attempted when the phone is to acquire/connect to a wireless network or system. In various embodiments, the phone may be configured to attempt to acquire network access (i.e., attempt to locate a channel or frequency with which it can access the wireless/communication network) at initial power-on and/or when a current channel or frequency is lost (which may occur for a variety of reasons).

The phone may include pre-built in USIM, SIM, PRL or access point information. In an embodiment, the mobile device may be configured for first responders and/or public safety network by, for example, setting the incident radio system as the default and/or preferred communication system.

As mentioned above, despite recent advances in mobile and wireless communication technologies, determining the specific location of a mobile device 102 in a wireless network remains a challenging task for a variety of reasons, including the variability of environmental conditions in which mobile devices are often used by consumers, deficiencies in existing technologies for computing and/or measuring location information on mobile devices, and the lack of uniform standards. For example, there is currently no universally accepted standard for implementing or providing location-based services. As a result, mobile device designers and wireless network operators, in conjunction with local public safety and third party providers, are using a variety of inefficient, incoherent, and sometimes incompatible methods, technologies, solutions, and/or techniques to determine the location of a mobile device and/or to provide location based services.

While there are no universally accepted standards for implementing or providing location-based services, there are certain requirements or standards associated with determining the location of a mobile device that may be of use in various embodiments. The U.S. Congress has mandated that cellular service providers configure their networks, communication systems and/or mobile devices so that the locations of mobile devices can be determined when a 911 call is placed. To implement Congress's mandate, the Federal Communications Commission (FCC) requested cellular service providers upgrade their systems in two phases (herein “Phase I” and “Phase II” respectively). While the level of precision/accuracy provided by these Phase I and II upgrades are generally inadequate for providing effective location based services that meet the demands of modern users of mobile devices, these upgrades provide a foundation from which more effective location based solutions may be built.

As mentioned above, the FCC requested cellular service providers upgrade their systems in two phases. In the first phase (Phase I), cellular service providers were to upgrade their systems so that emergency calls (e.g., 911 calls) are routed to the public service answering point (PSAP) closest to the cell-tower antenna with which the mobile device is connected, and so that PSAP call-takers can view the phone number of the mobile device and the location of the connecting cell-tower. The location of the connecting cell-tower may be used to identify the general location of the mobile device within a 3-6 mile radius.

In the second phase (Phase II), cellular service providers were to upgrade their systems so that PSAP call-takers could identify the location of the mobile device to within 300 meters. To meet these Phase II requirements, wireless service providers have implemented a variety of technologies, and depending on the technology used, can generally identify the location of the mobile device to within 50-300 meters. For example, on systems that have implemented a network-based solution (e.g., triangulation of nearby cell towers, etc.), the location of a mobile device can be determined within an accuracy of 100 meters 67% of the time, and to within an accuracy of 300 meters 95% of the time. On systems that have adopted a mobile device-based solution (e.g., embedded global positioning system receivers, etc.), the location of the mobile device may be determined to within 50 meters 67% of the time, and to within 150 meters 95% of the time.

Existing phase I and II solutions, alone, are not adequate for generating location information having sufficient accuracy or detail for use in providing accurate, powerful, and reliable location based services. Various embodiments may use some or all of the capabilities built into existing systems (e.g., as part of phase I and II upgrades, device-centric systems, network-centric systems, etc.), in conjunction with more advanced location determination techniques, to compute location information suitable for the advanced location based services demanded by today's consumers.

In addition to the three basic approaches discussed above, a number of different solutions are currently available for determining the location of mobile device, any or all of which may be implemented by and/or included in the various embodiments.

Most conventional location determination solutions use distance estimation techniques that are based on single-carrier signals, and one of the fundamental operations in ground-based (or network-centric) location determination solutions is timing estimation of a first-arrival path of a signal. That is, a single-carrier signal transmitted between a transceiver and a mobile device can be received via multiple paths (i.e., multipath), and the multiple paths of the signal can have different received powers and arrival times. The received signal may be cross-correlated to distinguish the multiple paths of the received signal. In this method, it is generally assumed that the first-arrival path (e.g., first detected signal, strongest signal, etc.) is associated with the path traveling the shortest distance, and hence is the right value to use in estimating distance between the mobile device and the transceiver. Often, this first-arrival path is the strongest path due to zero or fewer reflections, relative to the other paths, between the transceiver and the mobile device.

In various embodiments, the first-arrival time of the identified first-arrival path may be used in addition to other parameters (e.g., an estimated signal transmission time and/or a time offset between clocks of the transceiver and the mobile device, etc.) to estimate distance between a mobile device and a network component (e.g., another mobile device, a transceiver, an access point, a base station, etc.). The first-arrival time may be estimated by the mobile device (e.g., based on the downlink received signal) or by the network component (e.g., based on an uplink received signal).

The location of the mobile device may also be determined by estimating the distance between the mobile device and a network component or other signal sources (e.g., a transceiver, ground or satellite-based signal sources, etc.). For example, the location of the mobile device may be determined by performing trilateration using estimated distances between multiple (e.g., three or more) transceivers and the mobile device.

Another location determination solution may include computing an observed time difference of arrival (OTDOA) value by measuring the timing of signals received from three network components (e.g., mobile devices, transceivers, access points, etc.). For example, a mobile device may be configured to compute two hyperbolas based on a time difference of arrival between a reference transceiver signal and signals of two neighbor transceivers. The intersection of the computed hyperbolas may define a position on the surface of the earth that may be used by various embodiments to determine the location of the mobile device.

The accuracy of such OTDOA solutions may be a function of the resolution of the time difference measurements and the geometry of the neighboring transceivers. As such, implementing an OTDOA solution may require determining the precise timing relationship between the neighboring transceivers. However, in existing asynchronous networks, this precise timing relationship may be difficult to ascertain.

In various embodiments, location measurement units (LMUs) may be added throughout a deployment region of an asynchronous network to measure/compute timing information for one or more network components (e.g., transceivers) relative to a high quality timing reference signal. For example, a mobile device or an LMU may determine the observed time difference between frame timing of transceiver signals, and the observed time difference may be sent to the transceiver or a radio network controller of the communication network to determine the location of the mobile device. The location of the mobile device may also be determined based on the observed time difference and assistance data (e.g., position of the reference and neighbor transceivers) received from the communication network.

Another location determination solution may include computing an uplink-time difference of arrival (U-TDOA) based on network measurements of the time of arrival of a known signal sent from the mobile device and received at multiple (e.g., four or more) LMUs. For example, LMUs may be positioned in the geographic vicinity of the mobile device to accurately measure the time of arrival of known signal bursts, and the location of the mobile device may be determined using hyperbolic trilateration based on the known geographical coordinates of the LMUs and the measured time-of-arrival values.

As discussed above, conventional location determination solutions are typically based on single-carrier signals. The various embodiments include a ground-based location determination solution based on multi-carrier signals. A location determination solution based on multi-carrier signals may improve the accuracy of the computed location information by, for example, improving the accuracy of the timing estimation (e.g., by expanding the bandwidth of cellular signals). Location determination solutions based on multiple carriers may be used in both the device-centric (e.g., mobile device-based) and network-centric (e.g., base station-based) approaches, and may be applied to both 3GPP and 3GPP2 wireless communication technologies.

In various embodiments, a mobile device may be configured to determine its geospatial location based on information collected from mobile device sensors (e.g. gyroscope, accelerometer, magnetometer, pressure sensor, etc.), information received from other mobile devices, and information received from network components in a communication system.

FIG. 4A illustrates an example communication system within which the various embodiments may be implemented. Generally, the mobile device 102 may be configured to send and receive communication signals to and from a network 406, and ultimately the Internet 114, using a variety of communication systems/technologies (e.g., GPRS, UMTS, LTE, cdmaOne, CDMA2000™). In the example illustrated in FIG. 4, long term evolution (LTE) data transmitted from the wireless device 102 is received by a eNodeB (eNB) 404 and sent to a serving gateway (S-GW) 408 located within the core network 406. The mobile device 102 or serving gateway 408 may also send signaling (control plane) information (e.g., information pertaining to security, authentication, etc.) to a mobility management entity (MME) 410.

The MME 410 may request user and subscription information from a home subscriber server (HSS) 412, perform various administrative tasks (e.g., user authentication, enforcement of roaming restrictions, etc.), and send various user and control information to the S-GW 408. The S-GW 408 may receive and store the information sent by the MME 410 (e.g., parameters of the IP bearer service, network internal routing information, etc.), generate data packets, and forward the data packets to a packet data network gateway (P-GW) 416. The P-GW 416 may process and forward the packets to a policy and control enforcement function (PCEF) 414 which receives the packets and requests charging/control policies for the connection from a policy and charging rules function (PCRF) 415. The PCRF 415 provides the PCEF 414 with policy rules that it enforces to control the bandwidth, the quality of service (QoS), and the characteristics of the data and services being communicated between the network (e.g., Internet, service network, etc.) and the mobile device 102. In an embodiment, the PCEF 414 may be a part of, or perform operations typically associated with, the P-GW 416. Detailed information about policy and charging enforcement function operations may be found in “3rd Generation Partnership Project Technical Specification Group Services and System Aspects, Policy and Charging Control Architecture,” TS 23.203, the entire contents of which are incorporated herein by reference.

In an embodiment, the network 406 may also include an Evolved Serving Mobile Location Center (E-SMLC) 418. Generally, the E-SMLC 418 collects and maintains tracking information about the mobile device 102. The E-SMLC 418 may be configured to provide location services via a lightweight presentation protocol (LLP), which supports the provision of application services on top of TCP/IP networks. The E-SMLC 418 may send or receive (e.g., via LPP) almanac and/or assistance data to and from the MME 410 and/or eNB 404. The E-SMLC 418 may also forward external or network initiated location service requests to the MME 410.

In addition, the mobile device 102 may receive information from the serving eNodeB 404 via System Information Blocks that includes the neighbor cells to scan that are on the same system using the same frequencies or different frequencies, Home eNB (HeNB), in addition to CDMA, GERAN and UTRA cells.

FIG. 4B illustrates logical components, communication links, and information flows in an embodiment communication system 450 suitable for use in determining the location of the mobile device. The communication system 450 may include a network location based system 452, a core network 454, and a radio access network 456. The communication system 450 may also include an application module 458, a position calculation module 460, a wireless grouping module 462, and a sensor data module 464, any or all of which may be included in a mobile device 102. The application module 458 (e.g., client software) may request and receive location information from the network location based system 452 (e.g., through the core network 454 and the radio access network 456). Likewise, the network location based system 452 (or another client attached to, or within, the core network 454) may request and receive location information from the application module 458.

In various embodiments, the mobile device 102 may be configured to determine its geospatial location based on information collected from mobile device sensors (e.g. gyroscope, accelerometer, magnetometer, pressure sensor, etc.), information received from other mobile devices, and information received from network components in a communication system. In an embodiment, the collection and reporting of sensor information may be controlled/performed by the sensor data module 464. For example, the application module 458 may retrieve/receive sensor information from the sensor data module 464 and send the sensor information to the position calculation module460 to compute the location of the mobile device locally for position updates and/or position augmentation. The application module 458 may also send the computed location information to the network location based system 452 and or other mobile devices.

As mentioned above, in various embodiments, the mobile device 102 may be configured to determine its geospatial location based on information collected from other mobile devices. In these embodiments, two or more mobile devices may be organized into groups. Each mobile device may also share its location information with the other mobile devices with which the mobile device is grouped. For example, mobile devices may be configured to share their current location and/or position information (e.g., latitude, longitude, altitude, velocity, etc.) and an estimate of a distance between themselves and a target mobile device with other mobile devices in their group.

In an embodiment, the grouping of mobile devices may be controlled by the wireless grouping module 462. For example, the application module 458 may retrieve wireless group information (e.g., information pertaining to the locations of other mobile devices) from the wireless grouping module 462, and send the group information to the position calculation module 460 to perform local calculations for position updates and/or position augmentation. In an embodiment, the position calculation module 460 may perform the local calculations based on both sensor information received from the sensor data module 464 and group information received from the wireless grouping module 462.

In an embodiment, the mobile device 102 may be configured to automatically share its location information with other mobile devices upon discovery of the other mobile devices. Mobile devices may augment their location information (e.g., position coordinates) with information received from other mobile devices within same geographic location, and in a controlled pseudo ad-hoc environment. Since the shared location information (e.g., latitude, longitude, altitude, velocity, etc.) involves a relatively small amount of data, in an embodiment the mobile devices may receive such information from a network server by in-band and or out-of-band signaling.

When implemented in a 3GPP-LTE network, the various embodiments may include an E-SMLC 418 component configured to send and receive location information (e.g., latitude, longitude, altitude, velocity, etc.) to and from the mobile devices, which may be achieved both on-net and off-net. The location information may be delivered in standard formats, such as those for cell-based or geographical co-ordinates, together with the estimated errors (uncertainty) of the location, position, altitude, and velocity of a mobile device and, if available, the positioning method (or the list of the methods) used to obtain the position estimate

To aid in the determination of the locations of mobile devices, 3GPP-LTE networks have standardized several reference signals. Various embodiments may use these reference signals for timing based location and positioning solutions. Such reference signals may include the primary and secondary synchronization signals and the cell specific reference signals.

As mentioned above, two or more mobile devices may be organized into groups. Mobile devices within the same group may be part of the same network, or may be associated with different networks and/or network technologies. The mobile devices within the same group may also operate on different network operating systems (NOSs) and/or radio access networks (RANs).

FIGS. 5A-5C illustrate functional components, communication links, and information flows in an embodiment method of grouping mobile devices and sharing location information between grouped mobile devices. With reference to FIG. 5A, after a mobile device 102 is powered on, the mobile device 102 may scan the airwaves for predefined and/or preferred radio frequency carriers and/or systems with which the mobile device 102 may connect to the network. If the mobile device 102 does not find an appropriate network with which it may connect (or loses its connection) the mobile device 102 may scan the airwaves for other radio access systems (e.g., mobile network, radio access point associated with a mobile device, etc.) to acquire (i.e., connect to) until a connection to a network/Internet 510 is established. These operations may also be performed in the event of a dropped call or power interruption.

The mobile device 102 may also begin acquiring GPS signals while scanning the airwaves for radio frequency carriers and/or systems. If the mobile device 102 cannot acquire GPS signals, a network component (not illustrated) may help determine the relative position of the mobile device 102 based on one or more of the location determination solutions discussed herein (e.g., based on the antenna used for the radio access point, the time delay, angle of arrival, etc.).

The mobile device 102 may acquire (i.e., connect to) an appropriate radio access system, radio frequency carrier and/or system via the mobile device's system acquisition system. In the examples illustrated in FIGS. 5A-5C, the mobile device 102 establishes a connection to a network 510 via an eNodeB 404. However, it should be understood that any or all of the communication technologies discussed above are contemplated and within the scope of the various embodiments.

After the mobile device 102 acquires the radio access system, the network 510 (i.e., a component in the network such as a server) will know the approximate location of the mobile device 102 (e.g., via one or more of the location determination solutions discussed above, such as proximity to base towers). In addition, the mobile device 102 may compute its current location (e.g., via GPS and/or the location determination solutions discussed above), store the computations in a memory of the mobile device, and report its current location to the network 510.

In addition to knowing the approximate location of the mobile device 102, the network 510 may also be informed of the locations of other mobile devices 502 and the proximity of the other mobile devices 502 to the recently acquired mobile device 102.

FIG. 5B illustrates that the network 510 may send instructions/commands to the mobile devices 102, 502 to cause the mobile devices 102, 502 to group with mobile devices 102, 502 and possibly others. In an embodiment, the network 510 may be configured to automatically group the mobile devices 102, 502 based on the proximity of the devices 102, 502 with respect to one another. In an embodiment, the network 510 may be configured to allow an incident command system (ICS) commander to group the devices. In an embodiment, the network 510 may be configured to allow the mobile devices to form groups based on their proximity to one another. FIG. 5C illustrates that the mobile device 102 may pair/group with another mobile device 502 and/or establish communication links so that the mobile devices 102, 502 may share real-time relative location information with each other. Two or more grouped/paired mobile devices 102 and 502 may identify their relative positions to each other by sending relative location information over the established communication links. The relative location information may include time-to-arrival, angle-of-arrival, and existing or self-aware location information.The mobile devices 102, 502 may be configured report sensor information to each other and/or the network 510. The sensor information may include x, y, z coordinate information and velocity information. The sensor information may be polled on a continuous basis, may be requested periodically, and/or made available on demand in response to network/system requests.

In an embodiment, a mobile device 102, 502 may be configured to report sensor information in response to determining that there is a high likelihood that there has been change in a location of the mobile device 102, 502 (e.g., in response to detecting motion). The mobile devices 102, 502 may also be configured collect and report sensor information to the network 510 in response to receiving an instruction/command from the network 510 (i.e., a component in the network such as a server or E-SLMC 418 illustrated in FIG. 4). The network 510(i.e., a component in the network) may be configured receive the sensor and location information from the mobile devices 102, 502, and compute and store information about the distances (e.g., in time delay and angle of arrival with respect to the mobile devices 102, 502).

In an embodiment, the reporting of sensor information may be based on local parameter settings. For example, the mobile devices 102, 502 may be configured to transmit sensor information when any of the measured parameters (e.g., x, y, z and velocity information) meet or exceed a threshold value (e.g., exceed a rate-of-change, meet a timeout limit), which may be identified by local parameter settings stored in a memory of the mobile devices 102, 502. In an embodiment, the mobile devices 102, 502 may be configured to re-compute and/or update their location information in response to determining that the measured parameters (e.g., x, y, and z coordinates and velocity information) meet or exceed a threshold value.

In an embodiment, a mobile device 102 and/or the network 510 (i.e., a component in the network) may be configured to compare collected sensor information to computed latitude and longitude coordinates, relative altitude information, and other available information to determine if there is a discrepancy between the collected/measured values and the expected values. When it is determined that there exists a discrepancy between the expected and measured values, the mobile device 102 and/or network 510 may perform additional measurements to improve the location accuracy of the measurements/location information.

FIG. 5D illustrates an embodiment mobile device method 550 for grouping mobile devices and sharing location information between grouped mobile devices and the network to compute enhanced location information. After a mobile device is powered on, in block 552, the mobile device may scan the airwaves for predefined and/or preferred radio frequency carriers and/or systems with which the mobile device may connect. In block 554, the mobile device may begin acquiring GPS signals while scanning the airwaves for radio frequency carriers and/or systems. If the mobile device cannot acquire GPS signals, the mobile device or a network component may, as part of block 554, determine the relative position of the mobile device based on one or more of the location determination solutions discussed herein. In block 556, the mobile device may acquire (i.e., connect to) an appropriate radio access system, radio frequency carrier, system and/or network.

In block 558, the mobile device may compute its current location (e.g., via GPS and/or the location determination solutions discussed above), store the computations in a memory, and report its current location to the network. In block 560, the mobile device may group with other mobile devices in response to receiving instructions/commands from a network component and/or in response to detecting that the other mobile devices are within a predefined proximity to the mobile device (i.e., within a threshold distance). In block 562, the mobile device may share its current location information, as well as information collected from sensors, with the grouped mobile devices. In block 564, the mobile device may receive location and/or sensor information from the grouped mobile devices. The sensor information may include x, y, z coordinate information, bearing, and velocity information.

In block 566, the mobile device may identify the relative positions of the other mobile devices, which may be achieve by evaluating the location and sensor information received from the other mobile devices and/or via any or all of the location determination solutions discussed herein. In block 568, the mobile device may send the relative location information, its current location information, and/or sensor information to a network component and/or the other mobile devices, which may receive the sensor and location information and compute updated location information (e.g., based on distance in time delay and angle of arrival, relative altitude information, etc.). In block 570, the mobile device may receive updated location information from the network component and/or the other grouped mobile devices. In block 572, the mobile device may update its current location calculation and/or information based on the information received from the network component and/or the other grouped mobile devices. The operations of blocks 562-572 may be repeated until the desired level of precision is achieved for the location information.

FIGS. 6A-6D illustrate functional components, communication links, and information flows in an embodiment method for computing location information in which the grouped/paired mobile devices 102, 502 are updated with their respective location information.

FIG. 6A illustrates that the mobile device 102 may communicate with a serving eNodeB 404 to relay its location information to the network 510 and/or to receive location information from the network 510.

FIG. 6B illustrates that another mobile device 502 may also communicate with the serving eNodeB 404 to relay its location information to the network 510 and/or to receive location information from the network 510.

FIG. 6C illustrates that the grouped/paired mobile devices 102, 502 may communicate with each other to determine the distance between each other, which may be achieved by the mobile devices 102, 502 communicating various types of information, such as time-of-arrival, relative position with angle-of-arrival measurements, and other similar values, measurements, or computations. The mobile devices 102, 502 may then re-compute, refine, and/or update their current location calculations and/or location information based on information received from the other mobile devices 102, 502.

FIG. 6D illustrates that the grouped/paired mobile devices 102 and 502 may send their self-aware location information and/or relative location information to the network 510 (via the serving eNodeB 404), and receive updated location information from the network 510. For example, the mobile devices 102 and 502 may send their present location coordinates, distances between mobile device (e.g., distance to each other), altitude, and bearings (e.g., where mobile device 102 is with respect to mobile device 502) to the network 220. The network may compute updated location information based on the received information (e.g., coordinates, sensor information, proximity information, etc.), and send the updated location information to the mobile devices 102, 502. The mobile devices 102, 502 may then re-compute, refine, and/or update their current location calculations and/or location information based on information received from the network.

The operations discussed above with respect to FIGS. 6A-6D may be repeated so that the mobile devices 102, 502 recursively, continuously, and/or periodically re-compute, refine, and/or update their current location calculations and/or location information based on updated information received from the other mobile devices and/or the network 510 until the desired level of precision is achieved for the location information.

FIG. 6E illustrates an embodiment system method 650 of determining the location of two or more grouped mobile devices. In block 652, a first mobile device may send and/or receive current location information to and from a network component. In block 654, a second mobile device may send and/or receive current location information to and from a network component. In block 656, the first and second mobile devices may communicate with each other to determine the relative distances between each other, which may be achieved by communicating various types of information, including time-of-arrival, relative position with angle-of-arrival measurements, velocity, altitude, etc.

In block 658, the first and/or second mobile devices may re-compute, refine, and/or update their current location calculations and/or location information based on information received from the other mobile devices and/or the network. In block 660, the first and/or second mobile devices may send their updated current location calculations and/or location information to the network component, which may receive the calculations/information and compute updated location information (e.g., based on distance in time delay and angle of arrival, relative altitude information, etc.). In block 662, the first and/or second mobile devices may receive updated location information from the network. The operations in blocks 658-662 may be repeated until the desired level of precision is achieved for the location information.

It should be understood that the methods and operations discussed above with reference to FIGS. 5A-5D and 6A-6F may also be performed such that they include more than two devices. For example, in an embodiment, the mobile devices may be grouped into units of four (4) such that each mobile device may triangulate its position relative to the other mobile devices in the same group.

In an embodiment, a mobile device 102 and/or a network component may store relative location information for all the mobile devices within each group, based on the type of grouping. For example, a network component may store relative location information for all the mobile devices grouped/paired by an incident command system (ICS) commander. Likewise, the network component may store relative location information for all the mobile devices grouped/paired based on their proximity to each another.

In an embodiment, the mobile device 102 may be configured to detect a low battery condition, and initiate operations to conserve battery. For example, a mobile device 102 may be configured to turn off its radio and/or terminate or reduce its participation in the group/pairing information exchange. As another example, a mobile device 102 may be flagged or identified as having a low battery condition, and the other grouped/paired mobiles devices may be informed of the low battery situation so that update intervals may be adjusted to reduce battery consumption.

FIG. 6F illustrates an embodiment method 670 of adjusting the update intervals in a mobile device in response to detecting a low battery condition. In block 672, the mobile device may detect/determine that the amount of power remaining in the mobile device battery is below a predetermined threshold. In block 674, the mobile device may transmit a signal or otherwise inform grouped mobile devices of the detected low battery condition. In block 676, may initiate operations to converse power, such as by turn off its radio and/or reducing its participation in exchanging information with grouped mobile devices. In block 678, the mobile device and/or the informed grouped mobile devices may adjust the update intervals with respect to the mobile device to reduce the load on the mobile device.

As discussed above, grouped mobile devices may share various types of information to improve the accuracy of the location determination calculations. For the information shared between grouped/paired mobile devices, a comparison may be made for the path, range, between the mobile devices using any or all of the information available to the mobile devices (e.g., location coordinates, sensor information, proximity information, etc.). If the two mobile devices report relative positional information that is within a user or network defined range tolerance as being acceptable this is information may be forwarded to the network. If the relative positional information is not within the user or network defined range tolerance, additional polling operations may be performed to improve the accuracy of the measurements or location information. The above-mentioned operations may be repeated until the desired level of accuracy is achieved. In an embodiment, the number of times the above-mentioned operations are repeated may determined based on a user-definable values which can be set by the network, user or algorithm used.

As mentioned above, a mobile device 102 may include two or more of the same type of sensor. In the embodiments in which the mobile device 102 includes more than one of the same type of sensor (e.g., includes two accelerometers), one of the sensors (e.g., one the two accelerometers) may be identified as a master sensor. The values measures by each sensor may be compared, and if the difference between the values falls within a tolerance range, the values measured by the master sensor may be used to compute the sensor parameters (e.g., x,y,z and velocity parameters). If the difference between the values falls outside a tolerance range, the mobile device may use information collected from other sensors (of the same or different types) to determine if the values measured by the master sensor are consistent with expected values. For example, the mobile device may use information collected from various other types of sensors to compute sensor parameters (e.g., x,y,z and velocity parameters), and compare the computed sensor parameters to similar sensor parameters computed based on the values measured on the master sensor to determine if the master sensor is functioning correctly. Values measured on the master sensor may also be compared to information stored in the network or other mobile devices to determine if the master sensor is functioning correctly. If it is determined that the master sensor is not functioning correctly, a secondary sensor may be designated as the master sensor. The previous master sensor may be demoted to standby status (i.e., for use if the primary sensor has a failure) and not used for immediate positional calculations.

As mobile devices move into an area, the mobile devices may be asked to group/pair with more devices. The number devices that a mobile device can group/pair with may be restricted by user configuration, through the system, and/or user intervention so as to conserve battery and computational efforts (e.g., when the mobile device detects a low battery condition).

In an embodiment, proximity grouping may be used in the x, y and z coordinates/fields and/or for velocity and acceleration information.

In the event that a mobile device is unable to group with another mobile device with which it is instructed to group/pair with (e.g., due to a RF path problems), the mobile device may group with yet another mobile device in an ad-hoc fashion. If no mobile device is pairable with the mobile device, it may rely on its own geographic and/or and sensor information to report to the network.

When a mobile device 102 is undetected as being within a given proximity of a grouping radius, other mobile devices in the same group as the mobile device 102 may be informed of the decision to degroup/depair them from the mobile device 102. In an embodiment, the system may be configured so that an approval from the incident commander or user is required before the mobile is degrouped/depaired. In an embodiment, this may be achieved may transmitting a signal to a mobile device of the incident commander or user requesting approval, to which the incident commander or user may send a reply approving or disapproving of the request to degroup/depair. In an embodiment, the degrouping/depairing process may be transparent to the mobile device users.

In the event that a mobile device is unable to communicate with the network, the mobile device may send telemetry information pertaining to location services (and other telemetry information) to a grouped mobile device for relaying to the network.

In an embodiment, polling for information may be performed once the network has lost communication with the mobile device. Mobile devices that and known to be grouped to the mobile device may be instructed to communicate with the disconnected mobile even when it is trying to reacquire the network. A logical sequence based on proximity, signal quality to the network, and/or battery strength may be used to determine which mobile device will be used as a relay for communicating with the network.

The relayed telemetry information may include more than just positional information. For example, the telemetry information may also include bio sensor and user bio information reporting on the environment and user conditions, including heart rate and temperature, CO, 02 and other sensor information.

In an embodiment, the network may continuously measure/monitor the connected mobile devices. Knowing their location and relative location to each of the other mobile devices enables the network to continuously measure the uplink and downlink communication paths. If a communication path degradation occurs and begins to fall within a defined system quality range (which may be user defined), a mobile device may be instructed to either handover to another radio access node for the same network and/or network technology, or be instructed to initiate to perform relay operations to relay communications though a defined mobile device as a secondary signal path.

In the event that a communication link is lost with the network the mobile device may attempt to acquire itself on another network. While the acquisition process is underway, a mobile device may act as a mesh device. Other mobile devices in the proximity group may also connect as a mesh network.

In an embodiment, the mobile devices may utilize dead reckoning techniques to compute location information. Mobile devices may store the information for use in calculating more accurate location information, and may eventually relay to another mobile device which has network access or until one of the mobile devices or both devices have access to the initial network or another network and granted access to whether it is public or a private network.

FIG. 7 illustrates normal operating conditions in which a mobile device 102 will periodically scan for other cells 704, including its serving cell 903. If the radio access points are part of the network then the mobile device will report the identity and signaling information required by the existing network to determine (e.g., via triangulating and/or trilateration) the mobile device's location based on a network approach. If the mobile device detects a radio access point is not part of its preferred cell selection process, it may attempt to read the coordinates and positional information from the access point that is broadcast.

Once synched with the access point the mobile device may determine the timing difference and other requisite information to help determine its relative location and distance from the access point. This information may be related to the location system used by the mobile device to help refine its current location calculations.

Additionally the mobile device may be configured to compare each cell read to its own coordinate and using bearing and time difference for all the cells it reads. The mobile device may then triangulate on its own position.

During a 911 call a software application on the distressed mobile device may be executed. The software application may access an active neighbor list, read the overhead of each cell, and use that information to triangulates on the mobile device's own positions. The mobile device may also read the time offset for each of the cells.

In this case the system begins to try and locate the distressed mobiles position with more precision an accuracy to assist First Responders with triangulating on the distressed mobiles position and sending the information to the incident commander and/or public service answering point (PSAP) with a relative distance to target indication that is updated on pre-defined intervals. If the mobile device has lost contact with the 911 center, PSAP then the last location is continuously display and any velocity information is also relayed to assist the first responders.

In an emergency, the mobile device 102 may be configured to send its location information to the network. The mobile device 102 may be configured to automatically send its location information in response to detecting the emergency, or may provide the user with an option to send the location information. In an embodiment, the mobile device 102 may be configured to send its location information in response to a network initiated command.

Each mobile device may become an access point (AP). The decision to be the access point may be periodically updated while still in communication with the network, or when no network is found. Upon powering up, each mobile device may act as a client, and on a pseudo random time interval, the mobile devices may become an access point and then a client.

The location based methodology may be the same for a frequency-division duplexing (FDD) and a time-division duplexing (TDD) system. However in the event that the communication link between the mobile device and the network is lost, the mobile device may be configured to relay its telemetry information through another mobile device having network access.

In an embodiment, all information sent via wireless communication links may be digital. In an embodiment, the information may be encrypted to a requisite advanced encryption standard (AES) standards level or the appropriate encryption level needed for the requisite communication system and access method used.

Generally, the location based systems (LBS) may utilize reactive or proactive based methods. In a reactive location based system, the mobile devices may synchronously interact with each other on a time basis or some other predetermined update method. In a proactive location based system, the mobile devices may update their location information based on a set of predetermined event conditions using an algorithm. The various embodiments may include both reactive and proactive aspects, taking the best of both approaches to enhance location accuracy and precision.

Various embodiments may include location determination solutions that utilize horizontal data (i.e., a set of reference points on the Earth's surface against which position measurements are made) and/or vertical data. Horizontal data define the origin and orientation of the coordinate system and are prerequisites for referring a position relative to the Earth's surface. Vertical data are based on geoids, which primarily serves as a basis to determine the height of a position relative to means sea level for which the geoids act as a benchmark for origin and orientation. Various embodiments may utilize horizontal and vertical data to provide/generate enhanced three dimensional location information. The horizontal and vertical data can be global, national, local or custom depending on the locality and positioning reference system utilized.

Traditionally global data are used for position location as compared to a local datum. Global data are used for initial position fixing if possible and are based on GPS coordinates. Local data are based on a particular position on the surface of the earth, which allows for a non GPS based location based services to take place. The various embodiments may use global data, local data, or both. In an embodiment, GPS may be used to help identify the initial positional fix, and may be augmented by dead reckoning and a hybrid trilateration solution that utilizes both network and terminal based positioning In this embodiment, both local and global data may be used. If GPS determined position information is unavailable then the initial position may be set to the same position as that of the reporting mobile or a distance that is estimated using received signal strength indication (“RSSI”) and/or time of flight such that 0.5 of the estimated distance is applied to the horizontal component and vertical component of the reporting mobile device and the altitude, which may also be reported by the additional mobile devices.

Generally, a hybrid lateration and trilateration solution includes a mobile device performing a measurement and sending it to the network, and a network component performing the location determination calculations. The various embodiments include a hybrid lateration and trilateration solution in which the mobile device performs the location determination calculations, with and without the support of the network components.

Various embodiments may include sensor fusion operations in which a collaborative approach is used so that the sensors do not act as individual sensors, but as a collective team. As discussed above, the mobile device may include various sensors (e.g., accelerometer, gyros, magnetic compass, altimeters, odometers, etc.) capable of generating heading, orientation, distance traveled, and velocity as part of the sensor information collected on the mobile device. In various embodiments, information collected from any or all the internal sensors may be used for improving location or positioning accuracy and/or confidence improvements. Various embodiments may compute location information based on information from multiple sensors, with or without the aid of radio frequency propagation information.

The sensor fusion operations may include the sharing of telemetry including sensor data indicating relative movement of the individual mobile device, which enables temporal readings to assist in the location estimate, either with external assistance or dead reckoning

FIG. 8 illustrates an embodiment mobile device method 800 for determining the location of a mobile device in a wireless network. In block 802, a mobile device may determine its current location using any of the above mentioned location determination solutions to produce a final location estimate. In block 804, the mobile device may share its location information (i.e., the final location estimate) with other grouped mobile devices and/or receive location information from other grouped mobile devices. In block 806, the mobile device may compute and send a final location estimate, updated distance vector and sensor information to a network component for improved positional fix. In block 808, the mobile device may receive updated location information from the network component, and perform its own positional fix based on mobile data information received from the network. In block 810, the mobile device may update its location information and/or confirm its location information using dead reckoning to enhance positional accuracy.

Dead reckoning may provide the needed positional corrections as a local datum method for positioning when GPS or other network related positioning solutions are not available. Additionally dead reckoning may enhance the location position accuracy and precision calculations by providing and additional horizontal and vertical datum comparisons.

With dead reckoning, the current position may be deduced (or extrapolated) from the last known position. The dead reckoning accuracy requires a known starting point which either can be provided by the network, GPS, near field communication link, RF beacon, a predetermined zero position, or via another mobile device. For example, if GPS initial position information is available then the initial position may be set to zero or a distance that is estimated using RSSI or time of flight between another mobile device such that 0.5 of the estimated distance between the mobile device and a measurement location (e.g., another mobile device) is applied to the horizontal component and vertical component of the reporting mobile device and the altitude, which may be reported by the additional mobile device

A dead reckoning system may be dependent upon the accuracy of measured distance and heading, and the accuracy of the known origin. However the problem with relying on dead reckoning alone to assist in positional improvement is error accumulation caused by sensor drift (i.e., differences or errors in values computed/collected from one or more sensors). In particular, magnetic, accelerometers and gyroscopes are susceptible to sensor drift. The error accumulation for any of the sensors may increase over undulating terrain, as compared to flat terrain. Bias error and step size error are leading contributors to dead reckoning errors.

Various embodiments may tightly couple the mobile device sensors and continuously recalibrate the sensors to reduce any drift problems caused by unaided dead reckoning Additionally, as part of the tightly coupling the sensors, any bias drift associated with the sensors (e.g., a gyroscope) may be address by utilizing a kalman filter to reduce the errors from the primary and/or secondary sensors (e.g., gyroscopes).

In various embodiments, the mobile device may be configured to include velocity computations as part of the location determination computations to account for position changes that occur. When a GPS signal is available, the step size (via velocity computation) and compass bias errors may be estimated by an Enhanced Kalman Filter (EKF). Additionally if GPS is available, the compass may also be able to identify slow motion changes due to changes in magnetic inclination. The compass may be relied upon for motion computations in addition to that of accelerometers and gyroscopes, with and without the availability of GPS.

Dead reckoning accuracy degrades with time, requiring regular position updates or positional corrections. Therefore, the mobile device may be configured to not only use its own internal sensors to compute the location/positional information, but may also communicate with other mobile devices to leverage their location/positional information to enhance its own location/positional information. In essence, the mobile devices may act as RF base stations, proving the lateration capability to improve the positional accuracy of other mobile devices.

In an embodiment, a mobile device may be configured to poll one or more other mobile devices to gain a better positional fix on its location.

Mobile devices may be grouped together, either through assignment by the network or through the mobile device acquiring/detecting/connecting to other mobile devices (which may or may not be in the same network) as part of a discovery method for sharing location information.

Location information may be shared via the use of a near field communications system (e.g., Bluetooth®, ultrawideband, peanut radios, etc.), infrared, ultrasonic, and other similar technologies, such as via the use of WiFi. The wireless communications may also be ad hoc or infrastructure based, or based on a TDD system, such as LTE, SD-CDMA, TD-CDMA, or any other TDD methods.

In an embodiment, the mobile device may be configured to initiate the sharing of location information in response to receiving a network-driven grouping request from a network component.

In an embodiment, when the mobile device has lost contact with the network, it may attempt to find a suitable mobile device to help in its location determination computations, and for possible connection to the network (e.g., via a relay).

In an embodiment, the mobile device may be configured to send a request for location information to another mobile device. The request may be sent after the authentication process between mobile devices, and may include a time stamp which may be sub-seconds in size (milliseconds). Another mobile device may respond with a message that also has its time stamp and when it received the time stamp from the initiating mobile device.

Several messages (e.g., three messages) may be exchanged quickly between the mobile devices to establish time synchronization and share location/positional information that includes horizontal, vertical, and altitude coordinates (e.g. x, y, and z coordinates), a velocity, and acceleration component in each message. The time differences along with the x, y, and z coordinates may be compared with possible pulses or pings to establish an estimated distance vector between the devices.

When the distance vector and the x, y, z coordinates of two mobile devices are known, a point-to-point fix may be established. This process may be repeated for all the mobile devices in a group that has been assigned or created by the mobile device itself. Having multiple distance vectors from other points to the mobile will enhance the positioning accuracy.

A mobile device may be configured to report back to the network location server the distance vectors it has found between different mobiles. The other mobile devices also involved with the positioning enhancement may also report their distance vectors to the network to have their overall position accuracy improved as well.

The positional accuracy is meant to be done in incremental steps and the process will continue until no more positional improvements will be achievable. The positional accuracy improvement threshold may be operator defined, and may be stored in a mobile device memory.

When collecting the distance vectors and other positional information, if the error in position is greater than x % for a lower positional confidence level then no update may be required. As the mobile device receives other sensor data and more than a pre-described distance in any direction or a combined distance vector than the positional update process begins again. However if the x % of positional confidence level is less than desired additional positional, updates may be made with the mobile devices grouped together in an interactive process to improve the confidence level of the positional information.

It is important to note that typical positional location methods that are used currently by the network are not necessarily replaced with above-described positional lateration. Instead, the hybrid lateration method may be used in various embodiments to augment the positioning accuracy and confidence for network based position request due to boundary changes or paging requests or other position location triggered events.

FIGS. 9A-9E illustrate various logical components, information flows and data suitable for use in various embodiments. FIG. 9A illustrates that mobile devices 901, 902, 903, and 904 are communicating with the wireless network via multiple cell sites/radio access points/eNodeBs 911. The mobile devices 901, 902, 903, and 904 may compute a relative fix on their initial location using any of the location determination solutions discussed above. A first mobile device 901 may be instructed to find and communicate with the other mobile devices 902, 903 and 904, and/or any or all of mobile devices 902, 903 and 904 may be instructed to communicate with the first mobile device 901. The mobile devices 901, 902, 903, and 904 may be grouped together (e.g., via one of the grouping methods discussed above). The network may also designate one of the mobile devices 901 (e.g., a mobile device having a high position confidence) to be used as the reference or beacon for the other mobile devices 902, 903, and 904 within the group of mobile devices 901, 902, 903, and 904.

FIG. 9B illustrates that a combination of circular and hyperbolic trilateration operations may be performed as part of an embodiment location determination solution. For example, if any of the coordinate data provided by the sensors and/or mobile devices is in latitude and longitudinal coordinates, it may be converted to Cartesian coordinates to facilitate a hybrid lateration calculation. In the example illustrated in FIG. 9B, the mobile devices 901 has been designated as reference mobile device, reference number 912 identifies the position to be determined/computed (i.e., with a high level of accuracy) with respect to mobile device 901, reference number 910 identifies a three dimensional sphere that encompass the mobile device 901, and reference number 914 identifies an area of the three dimensional sphere (with x, y and z coordinates) within which the device exists.

FIG. 9C-9D illustrate that distance vectors may be computed between the mobile devices 901, 902, 903, and 904 as part of an embodiment location determination solution. In FIG. 9C mobile 901 using the hybrid trilateration method determines is relative position with respect to mobile devices 902, 903 and 904 respectively, Additionally, reference numbers 915, 909, and 916 identify the relative areas of mobile devices 902, 903, and 904, respectively. As part of the hybrid trilateration operations of the embodiment location determination solution, mobile devices 902, 903, and 904 may locate mobile device 901, and the mobile device 901 may compute a distance vector between itself and mobile devices 902, 903 and or 904. The mobile device 901 may initiate communications with mobile device 902 (although mobile device 902 could initiate the communication) and exchange time stamps, positional information, sensor data. The same process may occur with respect to mobile devices 904 and 903, in which positional and sensor information is exchanged.

As illustrated in FIG. 9D, the mobile devices 902, 903, and 904 may establish a distance vector between themselves and mobile device 901. The same process may occur with respect to mobile devices 902, 903 and/or 904, in which positional and sensor information is exchanged. Where mobile device 902 undergoes the same process as that done with mobile device 901 as part of the hybrid trilateration process, mobile device 901 may use mobiles 902,903, 904 to enhance it positional information and mobile device 902 may use mobiles 901,903 and 904 to enhance its positional information, and so forth for all the mobile devices that are grouped together.

The three circles or ellipses 909, 915 and 916 illustrated in FIG. 9C and the three circles or ellipses 906, 907 and 908 illustrated in FIG. 9D do not intersect at a given point, but span an area of a particular size depending on the range involved.

FIG. 9E illustrates an embodiment hybrid trilateration method in which the position of mobile device 901 is validated or improved upon. As part of the hybrid lateration method, separate calculation operations may be required for each set of x, y and z coordinates, in addition to accounting for velocity and acceleration. However, the ability to have three mobile devices 902, 903, and 904 locate mobile device 901 may present an error window (or an error area) for each coordinate plane represented by reference number 930. The error window/area may be a combination of range errors from the mobile devices 902, 903, and 904. Contributing to the error window/area is the hybrid range errors illustrated by reference numbers 921, 922 and 923, where: reference number 921 is the hybrid range error associated with mobile device 902; reference number 922 is the hybrid range error associated with mobile device 903; and reference number 923 is the hybrid range error associated with mobile device 904. Additionally this process can be done with less or more mobile devices than used in the above example.

For each axis(x, y, or z), a similar process occurs where the error area 930 is a combination of determining the range between the other mobile devices and mobile device 901. The hyperbolic lateration is a typical calculation method used in location based systems and is based on the principal that the range between two locations is the same. However the range determined for the points may not be constant since both can be moving toward, away or together at a similar velocity and trajectory.

With the hybrid lateration method proposed a corrective distance vector Δx,Δy,Δz is used that can be used to apply to the estimated position.

The three circles or ellipses 909, 915 and 916 illustrated in FIG. 9C and the three circles or ellipses 906, 907 and 908 illustrated in FIG. 9D do not intersect at a given point, but span an area of a particular size depending on the range involved. Therefore range is “r” and is denoted by the subscript representing the distance vector involved. Thus:

r=p _(i)+error

The pseudo range p_(i) deviated from the actual range in any axis due to the inaccuracy in synchronization or propagation in a multipath environment or due to sensor induced errors. Where the distance vector accounting for change in direction is:

r _(i)=√(X _(i) −x)²+(Y _(i) −y)²+(Z _(i) −z)²

Three range calculations are then averaged to determine the distance vector that is used. If the previous range calculation r_(j) as compared to that of the current calculation has an error in excess of a user defined % or variant then the new measurement is disregarded. Included with the distance vector validation may be the fusion sensor information where expected position verse calculated may be included for the confidence interval.

Range difference=d_(ij)=r_(i) 31 r _(i)

An iterative process may be used for position improvement, which may include the use of a least squares calculation fit to approximate the position solution in a step wise basis. The process may continue until the range difference measured does not produce any noticeable accuracy improvement, which may be user-defined, either at the mobile device or network or both.

The multi-lateration calculations may include estimating a location of a mobile device based upon estimated distances to three or more measurement locations (i.e., locations of three other mobile devices or wireless transceivers). In these calculations, the estimated distance from a measurement location (location of another mobile device) to the mobile device may be derived from the measured signal strength. Since signal strength roughly decreases as the inverse square of the separation distance, and the transmission power of the mobile device can be presumed, the distance d_(i) can be simply calculated as:

d _(i)=√(S ₀ /Si _(i))

where:

d_(i) is the estimated separation distance between a measurement location and the mobile device;

S_(i) is the measured signal strength; and

S₀ is the strength of the signal transmitted by the other mobile device).

Alternatively, the signal strength readings may be translated into distances using a path loss model, such as the following:

RSSI_(i) =a−cblog ₁₀(d _(i))

where:

a is the signal strength at d_(i)=1 meter;

b is the path loss exponent; and

c is the pathloss slope with 20 being used for free space.

The lateration operations may include performing a least squares computation, which may accomplished by a processor calculating the following formula:

min_((x,y))Σ(d _(i) −∥MS _(i)−(x,y)∥)²

where:

d_(i) is the distance calculated based on a measured signal strength value;

MS_(i) corresponds to the known location/position of the mobile device; and

the minimization value of (x, y) is the estimated position of other mobile devices.

In various embodiments, the velocity and acceleration of the mobile device with respect to the three or more reference locations (i.e., locations of three other mobile devices or wireless transceivers) may be determined along with the estimated distance from each reference location to the mobile device. The estimated distances to three or more measurement locations (i.e., locations of three other mobile devices or wireless transceivers), velocities, and acceleration calculations may have their components separated into a horizontal component set, a vertical component set, and an altitude component set respectively. As will be discussed in greater detail below, each of the component sets main contain the distance component, velocity component, and acceleration component for each of the reference locations. For example, an “x” component set may contain the distance x components, velocity x components, and acceleration x components associated with all reference locations. Some embodiments may include executing a kalman filtering procedure on the component sets individually to produce an estimated position of the mobile device.

FIG. 10 illustrates an embodiment hybrid lateration method 100 in which mobile devices may gain access to the network. The mobile devices may be instructed to be grouped by the network. Mobile devices 901 and 902 may initiate sharing of information for position location, either due to the network driven grouping request or when the mobile device has lost contact with the network and attempts to find a suitable mobile device to help in its position location and possible connection to the network via a relay or to another network.

Mobile device 901 may send a request for position information to mobile device 902. The information may be sent after the authentication process between mobile devices, and may include a time stamp. The time stamp may be sub seconds in size (e.g., milliseconds). The mobile device 902 may respond with a message that also has a time stamp, and timing information pertaining to when the mobile device 902 received the time stamp from mobile device 901. Three messages may be transferred quickly to establish time synchronization. The time differences may then be compared, along with possible pulses or pings, to establish an estimated distance vector between the mobile devices. Knowing the distance vector and the x, y, and z coordinates of both 901 and 902, a point-to-point fix may be established. In various embodiments, the position fix may be extrapolated to synchronize the trilateration time stamp with a time stamp of a dead reckoning calculation. Similarly, the time interval adopted for dead reckoning calculation updates may be adopted as the time interval between trilateration recalculations.

The mobile device 901 may then initiate communication with mobile devices 903, 904 and repeat the operations discussed above with respect to mobile device 902 for each of mobile device 903, 904. After obtaining two or more distance vectors along with positional information, the mobile device 901 may compare the new coordinates to its previously computed current location, and adjust the location computations accordingly.

The positional information distance vectors may be sent to the network for positional processing with other network positional information. Based on the position calculated for the mobile device, the network (i.e., a component in the network, such as a network server or E-SMLC) may instruct the mobile device to adjust its positional information.

Additionally the mobile device 901 may also make a positional correction if the network either does not respond in time, which may result in a message update time out. Alternatively, when the network cannot make the necessary correction, and the positional information may used by another component and/or other mobile devices to perform the necessary corrections.

If the error is greater than x % for a lower positional confidence level then no update is required. As the mobile receives other sensor data and more than a pre-described distance in any direction or a combined distance vector than the positional update process begins again. If the x % of positional confidence level is less than desired, additional positional updates may be made with the grouped mobile devices (e.g., iteratively) to improve the confidence level of the positional information. Additionally if the positional information from one of the mobile devices that is being attempted to obtain a distance vector appears to be in error, then that mobile devices data may be selected to not be used for this iterative step of performing positional updates with other grouped mobile devices. However it will continue to be queried as part of the process since its position location could be corrected in one of the steps it is taking to improve its position location as well.

Additionally in the event that one or more mobile devices lose communication with the core network it will still be possible to maintain position accuracy through one of the other grouped mobile devices. In some embodiments, the last position of the mobile device may be re-used and the amount of error increased. It will also be possible to continue to maintain a communication link by establishing a network relay connection with another of the mobile devices in the same group which still has communication with the network itself.

FIG. 11 illustrates another embodiment hybrid lateration method 100 in which a mobile device cannot locate a network due coverage problems. The mobile device 901 may operate in an autonomous mode and attempt to locate another mobile device. The other mobile device could be used to relay information to the network and possibly set up a near field communication bridge in addition to providing location enhancement capability.

In the example illustrated in FIG. 11, mobile device 901 establishes a near field LAN inviting other mobile devices in proximity to communicate with it. Positional information can then be shared and the mobile device 901 can have its location improved and the positional information can be relayed back to the core network via another mobile device.

The mobile device 901 may also communicate its positional information and establish near field communication link with a mobile device that is not part of the home network associated with mobile device 901.

The mobile devices may have the USIM, SIM, PRL or access point information pre-built in. The mobile device for first responders may have the incident radio system set as their preferred system, or in the case that the radio access system being used as a public safety network.

For first responders to utilize a wireless mobile network (e.g., LTE) the position location information accuracy needs to improved for in building environments in addition to providing more accurate location information about where the mobile devices are actually located. Whether the mobile device is used by a first responder, commercial cellular user or a combination of both.

The positional location improvement for first responders may be helpful to improve situation awareness, improved telemetry and overall communication with the incident commander. Since all incidents for first responders tend to be fluid the ability to account for a dynamic environment of mobile devices will come into and out of the incident area. In addition the mobile devices proximity location to other mobile devices can and will change as the incident situation changes where resources are added and or reassigned as the need arises for operational requirements.

The use of network and terminal driven position enhancement techniques previously discussed may be exploited. The grouping of mobile devices may be done either as part of pre-plan, with intervention by the incident commander or driven from the commercial wireless network, public safety wireless network or local incident communication system (ICS) 1204 based on reported proximity of the mobile devices.

FIG. 12A illustrates that upon arriving at the incident scene, a mobile device 102 may recognize the existence of a local radio network 1202. If there is no ICS radio network 1204 with which the mobile device may connect, the mobile device 102 will continue to communicate via a commercial or other wireless network, 1202.

FIG. 12B illustrates that the mobile device 102 may determine that there is a valid local radio system 1202 with which it may communicate, and may have a priority access to small cell system 1204 a based on a preferred network and cell selection process the mobile device 102 has been instructed to use.

FIG. 12C illustrates that the mobile device 102 may transfer the connection from the local radio system 1202 to the small cell system 1204.

For first responders when a situation arises that requires finding a man down or responding to an emergency call (911) the location based process can be used to help in the search and rescue of the person.

FIG. 13A illustrates that the mobile device 102 may be identified by the network as being in distress via network monitoring of the mobile device 102 or via the mobile device transmitting a distress signal. The distressed mobile device 102 may determine that it has lost communication with the network, and may instruct the wearer/user to either disable or initiate a distress signal. The mobile device 102, upon initiation of a distress signal, may begin a grouping process previously defined.

FIG. 13B illustrates that the network 510 to which the serving eNodeB 404 is connected to may instruct a mobile device 1302 in the same group as the distressed mobile device 102 to report the last known location of the mobile device 102 and time stamp.

FIG. 13C illustrates that the network 510 may instruct additional mobiles devices 1304 to attempt to group with the distressed mobile device 102.

FIG. 14 illustrates that when the mobile device 102 is unable to communicate with the network 510, it may been operating under a dead reckoning process and continue to attempt to locate other mobile devices 1402, 1404 and group with them under an ad-hoc scheme.

Once the mobile device has been grouped, or is still connected to the network, the relative location of the mobile device will be sent to all the mobile devices that are in active search for that mobile device. The selection of which mobile devices will be searched may be determined by operator intervention and selection.

As discussed above, the various embodiments include methods, and mobile devices configured to implement the methods, of determining a location of a mobile device, which may include determining an approximate location of the mobile device, grouping the mobile device with a wireless transceiver (e.g., a second mobile device, etc.) that is in proximity to the mobile device to form a communication group, sending the determined approximate location of the mobile device to the wireless transceiver, receiving on the mobile device location information (e.g., a latitude coordinate, a longitude coordinate, an altitude coordinate, etc.) from the wireless transceiver, and determining a more precise location of the mobile device based on the location information received from the wireless transceiver. In an embodiment, the method may include grouping the mobile device with a plurality of wireless transceivers in proximity to the mobile device to form the communication group, and receiving location information from the plurality of wireless transceivers in the communication group. In some embodiments, the method may also include sending information relating to the determined more precise location of the mobile device and the received location information to a server, receiving updated location information from the server, and re-computing the more precise location of the mobile device based on the updated location information (i.e. the information received from the server).

As also discussed above (e.g., with reference to FIG. 8) a mobile device may be configured determine its current location using any of a number location determination solutions to produce a final location estimate, share its location information (i.e., the final location estimate) with other grouped mobile devices and/or receive location information from other grouped mobile devices, compute and send a final location estimate (and updated distance vector and sensor information) to a network component for an improved positional fix, receive updated location information from the network component, and perform its own positional fix based on mobile data information received from the network. The mobile device may then update its location information and/or confirm its location information using dead reckoning to enhance its positional accuracy.

Further embodiments may include methods, and mobile computing devices configured to implement the methods, of providing enhanced location based services. In these embodiments, the mobile computing device may be configured to determine an initial position, generate at least one set of local position information based on locally determined location information, receive location information from one or more external location tracking systems, generate at least one set of external position information based on the location information received from the external location tracking systems, receive distance information from multiple mobile devices in a communication group, generate proximity position information based on the location information received from the multiple mobile devices in the communication group, and generate a final location estimation set (a position, velocity, and acceleration value) based on the local position information, the external position information, the proximity position information, and the initial position. That is, the mobile computing device may use a combination of the determined initial position, the generated local position information, the generated external position information, and the generated proximity position information to determine or compute a final location estimation set. The mobile computing device may then use the final location estimation set to provide a location based service (e.g., an emergency location service, commercial location service, internal location service, lawful intercept location service, etc.).

FIG. 15 illustrates a method 1500 of determining the location of a mobile device via a wireless network in accordance with an embodiment. The operations of method 1500 may be performed by a processor or processing core in a mobile device 102. In block 1502, the mobile device 102 may receive location information, such as GPS or cell-based location information, from one or more external systems (e.g., an external location tracking system, GPS system, a base station, a network server, etc.). In block 1504, the mobile device may determine its initial position, which may include performing any or all of the above-described location or position determination techniques, algorithms, or methods to determine or compute an initial position value. For example, the mobile device may determine its initial position (or the initial position value) based on GPS coordinates and/or GPS-determined position information received in its GPS circuitry from a GPS system.

The initial position value may be an information structure that includes one or more information fields, component vectors, location information, position information, coordinate information, etc. Similarly, the mobile device may include, generate, compute, determine or use a dead reckoning position estimate value, a combined sensor position estimate value, a trilateration position estimate value, a final location estimate value, and various temporary position and error values, any or all of which may be information structures that each include one or more information fields, component vectors, location information such as coordinates, and/or other similar information. In some embodiments, the mobile device may include a dead reckoning module that is configured to determine whether a dead reckoning position estimate value is available, as well as to generate time stamps, use dead reckoning techniques to compute location information, set the dead reckoning position estimate value, and/or perform other similar operations.

In some embodiments, as part of the operations in block 1504, the mobile device may determine whether GPS data (e.g., GPS coordinates or GPS determined position information) is available in the device. The mobile device may also determine, generate, or compute an temporary position value in response to determining that GPS data is not available, and set the initial position value to the determined temporary position value (e.g., by performing a deep or shallow copy of the temporary position value information structure). In various embodiments, the mobile device may determine or compute the temporary position value and/or set the initial position value to zero (0), to the last known location stored in memory, or to the RSSI or time of flight with/between another mobile device such that 0.5 of the estimated distance is applied to the horizontal component and vertical component of the reporting mobile device. In some embodiments, the mobile device may also derive, determine, or compute an initial altitude value (or generate initial altitude information) based on information reported by other or additional mobile devices, and use this initial altitude value to compute the temporary position value and/or to set the initial position value.

In determination block 1506, the mobile device may determine whether a dead reckoning position estimate value is available, which may include determining whether the device may utilize one or more dead reckoning techniques to compute or generate location information for the mobile device based on information that is stored in its memory or based on information that may be collected from its components (e.g., sensors, etc.). In response to determining that a dead reckoning position estimate value is not available (i.e., determination block 1506=“No”), the mobile device may wait for a predetermined amount of time (or for the occurrence of an event, a trigger, notification, collection of data, etc.) and repeat the operations of determination block 1506. Said another way, the mobile device may wait until it determines that it has access to sufficient information to use dead reckoning techniques/methods to compute its current location and set the dead reckoning position estimate value, or until it times out, a timer expires, etc.

In response to determining that a dead reckoning position estimate value is available (i.e., determination block 1506=“Yes”), the mobile device may determine, compute, or calculate an error value (e.g., in the form of a variance, etc.) that identifies the error associated with the determined local-position or dead reckoning estimate value in block 1508. In determination block 1510, the mobile device may determine whether a combined sensor position estimate value is available (e.g., is determinable from information collect from sensors or stored in memory, etc.) for the current time. In some embodiments, the mobile device may be configured to calculate/determine the inputs for computing or determining the combined sensor position estimate value by filtering the output of environmental sensors, such as the accelerometer, barometer, gyro, magnetometer and/or thermometer, and using the filtered sensor outputs as the inputs to an algorithm or method for computing the combined sensor position estimate value. In some embodiments, in block 1510, the mobile device may execute or perform a single variable kalman filter using the combined or “fused” sensor outputs to obtain, determine, or compute the combined sensor position estimate value. The single variable kalman filter may be a procedure, algorithm, method, technique, or sequence of operations for accomplishing the function of a kalman filter.

In response to determining that combined sensor position estimate is not available for the current time (i.e., determination block 1510 equals, or evaluates to, “No”), the mobile device may determine, compute, or calculate an error value (e.g., in the form of a variance value, etc.) that identifies an error/variance associated with the external position estimate in block 1512. For example, in block 1512, the mobile device may extrapolate the last available combined sensor position estimate value to fit a time stamp established or determined by the dead reckoning module of the mobile device. In some embodiments, the mobile device may be configured to increase or increment the error/variance value associated with the combined sensor position estimate value so as to compensate for a potential inaccuracy of the calculation. Said another way, the mobile device may be configured to update the combined sensor position estimate error value in block 1512 to better account for potential inaccuracies in its computations or location/position determinations.

In response to determining that combined sensor position estimate value is available for the current time (i.e., determination block 1510 equals, or evaluates to, “Yes”), the mobile device may determine an error/variance value for the external position estimate value in block 1514. Said another way, in block 1514 the mobile device may determine, compute, or calculate an external position estimate error value in response to determining that a combined sensor position estimate value is available.

In determination block 1516, the mobile device may determine whether a trilateration position estimate value for a current time is available. As described above, the mobile device may obtain more accurate position information by calculating its own location based on the distance between the mobile device and three or more reference locations. Some embodiments may include obtaining distance information from several reference locations and executing a kalman filter on the obtained coordinates to produce a single estimate of position, velocity, and acceleration in block 1516. In response to determining that a trilateration position estimate value for the current time is not available (i.e., determination block 1516 equals or evaluates to “No”), in block 1518 the mobile device may extrapolate the last available trilateration location information to a current time stamp established by the dead reckoning module. In response to determining that the trilateration position estimate value for the current time is available (i.e., determination block 1516 equals or evaluates to “Yes”), the mobile device may determine the error/variance associated with the trilateration position estimate value in block 1520.

In block 1522, the mobile device may compute, calculate, or determine a final location estimate value based on the dead reckoning position estimate value, the combined sensor position estimate value, the trilateration position estimate value, and the location information received from external systems (e.g., GPS coordinates, etc.). In various embodiments, the mobile device may set the values of one or more fields in the final location estimate value to be equal to the product of executing a kalman filter on the sets of location information/values, which may include (or may be based on) a position information estimate/value, velocity information estimate/value, and an acceleration information estimate/value. In block 1524, the mobile device may update the a final location estimate value with the calculated estimate/values and/or calculate or determine a gain value.

In block 1526, the mobile device may wait for a pre-determined amount of time (or wait until a time interval lapses) return to block 1516 to repeat the operations of method 1500 to begin updating the location information for the current time. In some embodiments, the time interval may be synched to the time of the dead reckoning module. Thus each subsequent updating of the dead reckoning location information may represent the beginning of another final location information recalculation.

FIGS. 16A-16B illustrate embodiment mobile device methods 1600 for calculating location information estimates. The operations of method 1600 may be performed by a processor or processing core in a mobile device 102.

With reference to FIG. 16A, an embodiment method for implementing a trilateration technique may include the execution of a kalman filter on distance information to produce a more accurate location, velocity, and acceleration estimate. In block 1602 the mobile device may initialize a starting point in the same manner as described with reference to block 1502 of FIG. 15. In block 1604, the mobile device may receive distance/location information from three or more reference locations (e.g., other mobile devices, access points, etc.) and may calculate a distance between the mobile device and each of the reference locations. The mobile device may further calculate a velocity and acceleration of the mobile device with respect to each of the reference locations. In block 1606, the mobile device may check for the presence of received inputs from at least three separate reference locations. If the mobile device has received location information from at least three reference locations then the process may proceed to block 1610. If the mobile device has not received a sufficient number of inputs from the reference locations, it may in block 1608 wait to receive further input. In some embodiments, while the mobile device waits for additional reference location information, it may update previous trilateration information according to the embodiment method 1700 described below with reference to FIG. 17.

In block 1610, the mobile device may calculate an error or variance associated with the distance calculation. In block 1612, the calculated distance information, along with the associated velocities and acceleration calculations may be separated into their directional components and grouped into vectors with like components from each reference location. For example, the calculations may be separated into Cartesian or radial coordinate components, and a z component vector may contain the z components of the distance, velocity, and acceleration information for all location information received from the at least three reference locations. In blocks 1614, 1616, and 1618, a kalman filter may be executed on each of the component vectors. In block 1620, the component vectors of all the kalman filter functions may be combined to calculate a three dimensional position estimate in the manner described with reference to embodiment method 1800 and FIG. 18.

In block 1622, the processor of the mobile device may update the trilateration position information with the estimate calculated in block 1620, and may further calculate a process gain. In block 1624 , the mobile device may wait a predetermined interval of time, as established by the time stamp of the dead reckoning module, and may then return to block 1604, to begin updating the trilateration location information.

FIG. 16B illustrates an embodiment mobile device method for calculating location estimates and proceeds in the manner described with reference to FIG. 16A above, but does not receive information from reference locations. In block 1602, the mobile device may initialize a starting position for the combined sensor location information calculation. In block 1630 the mobile device may collect data generated by local sensors such as an accelerometer, barometer, gyro, magnetometer and thermometer. The various sensor outputs may include position, velocity, and acceleration information and may be utilized by the mobile device in the same manner as the trilateration inputs. In blocks 1606 through 1624, the method may proceed as described above with respect to steps 1606 through 1624 of FIG. 16A. In some embodiments, the combined sensor location information may be used as input for dead reckoning

FIG. 17 illustrates an embodiment mobile device method for updating location information. From block 1608 of embodiment method 1600, there may be a lack of sufficient input for a given time interval to permit accurate location calculation. In block 1702, the mobile device may extrapolate a previous set of location information (e.g., position, velocity, and acceleration information) to a current time interval and. The error associated with the calculation may be increased in block 1704. The resulting location information and adjusted error may be returned to block 1604 for use in determining an updated set of location information.

FIG. 18, illustrates an embodiment mobile device method of executing a recursive, linear filter on location information. Using an initialized starting position, the filter may generate a new state estimate by combining new measurements with a predicted state estimate based on the measurements of a preceding time interval. At each iteration, location estimates are produced using only the new set of measurement vectors and values stored from the previous cycle. In some embodiments, the state three measurements for each input may be stored to facilitate calculation of a three dimensional position. Through multiple iterations of the filter, process noise, such as less accurate location information is removed from the produced final location estimate.

During the filter process, the state estimate “L” may be considered to be current for a time “k” or predicted for a time “k-.” The state, or location estimate may be a nine element vector including an estimated position, velocity, and acceleration for each coordinate component of a coordinate system. For example, a location estimate for a Cartesian coordinate system may be represented by the expression.

L_(k)=[x, y, z, v_(x), v_(y), v_(z), a_(x), a_(y), a_(z)]

In various embodiments, the vector may be separated into multiple smaller vectors including all elements associated with a coordinate component, thereby enabling easy isolation of position, velocity, and acceleration information with respect to a single axis of translation. Thus, “L_(k)” may e represented by the series of expression:

L_(xk)=[x , v_(x), a_(x)]

L_(yk)=[y, v_(y) , a_(y)]

L_(zk)=[z, ,v_(z), a_(z)]

The current state “L_(k)” may depend only on the predicted state for the previous time interval, new input measurements “G” (e.g., a new location information input), a transition matrix “H” and the kalman gain “K.” Thus, the current location estimate may be represented by the function:

L _(k) =L _(k) ⁻ +K _(k)(G _(k) −H.L _(k))

To begin location estimate calculation, a starting position “L₀” and associated covariance “P₀” may be initialized using GPS coordinates or local sensor output for a position, velocity, and acceleration. The estimated covariance matrix “P” may comprise the variance of an Lvector along the diagonal and the off diagonal elements set to zero. If no sensor output location information or externally provided starting coordinates are available, the initial values may be set at either 0 or an estimated distance using RSSI and or Time of Flight (TOF) from a reference location (e.g., another mobile device), such that 0.5 of the estimated distance will be applied to the X and Y component of the reporting reference location and the altitude, Z, will use as that reported by the other reference locations.

In block 1802 an error or covariance P may be determined, along with weight factors. Block 1802 may be the end of the prediction phase of the kalman filter process and may pass a predicted location, covariance, and weights to block 1804, the beginning of the updating phase. Once the starting point is initialized the starting position may be used as a predicted measurement and may be represented by the function:

L _(k) =L _(k) ⁻ +K _(k)(G _(k) −H.L _(k) ⁻)

Where “A” and “B” are state transition matrices that may map a state from a time “k−1” to “k. The parameter w_(k−)1 is a vector of elements representing the uncertainty in the noise for each of the parameters in the state vector L, and “u_(k)” is a parameter nulled out during calculation. The mean noise “w” along with a standard deviation of the noise “v_(k)” may enable convergence of location and sensor input datum, which are non-Gaussian. Current measurements “G” may be expressed as a function of the standard deviation by “G_(k)=HL_(k)+v_(k)” and alternatively as “G_(k)=H.L_(k)+R” where “R” is a matrix representing the variance of the measurements. The estimated covariance may be represented in terms of a previous covariance estimate, the transition matrix A, and a covariance matrix “Q” and expressed by the function:

P _(k) ⁻ =AP _(k−1) A ^(T) +Q

In block 1804, updating phase may begin, and the location estimate “L” may be updated. This phase ay begin by computing the Kalman gain “K” for the current time interval. The gain may be a product of the estimated covariance, and the measurement variance “R,” and may thus be represented by the expression:

K _(k) =P _(k) ⁻ .H ^(T).(H. P _(k) ⁻ .H ^(T) +R)⁻¹

The current location estimate may be updated using the current location estimate expression. Kalman gain depends on the current state estimate and the accuracy of the measurements. As the accuracy of the measurements increase the Kalman gain will be high placing higher weight on the measurements. In some embodiments, the time k may be larger, thus decreasing the accuracy of the measurements (due to staleness of the measurement) resulting in a low Kalman gain placing more weight on prediction than on the measurements. With a high gain, more weight will be placed on measurements than on the predicted value. In block 1806, the current covariance “P” may be computed. Over multiple iterations of the filter, the off-diagonal elements of the covariance matrix may become non-zero elements. The current process covariance may be expressed by the function:

P _(k)−(1−K _(k) H).P _(k) ⁻

The current location “L” and a current process covariance matrix P may be the outputs of the updating phase and may be passed as input to the beginning of the prediction phase in block 1808 for the next iteration of the filter. In block 1808, the mobile device may predict the location matrix/matrices and process covariance “P” matrix for a time “k+1” may and pass them as input to the second step of the predicting phase in block 1802. The current location estimate may also be used as current position, velocity, and acceleration information for location based services, and may be passed to the network in accordance with various embodiments. In some embodiments, the current location estimate may be passed as input to a second kalman filter and combined with other location inputs to produce a final location estimate.

In various embodiments, the systems, methods, and devices may execute a kalman filter in three separate instances within the embodiment method 1500 described with reference to FIG. 15. These instances may include execution of a kalman filter on the combined sensor location information, the trilateration location information, and the final location estimate, which combines the sensor location information, trilateration location information, and all other location input sources. For each instance, the inputs must be synchronized with the time stamp used by the dead reckoning module and may be extrapolated to fit a current dead reckoning time.

In various embodiments, the mobile device may utilize a kalman filter process to determine accurate location information for a mobile device. Multiple kalman filters may be executed on location information from varied sources. The output of filtering location information from multiple sources may be input for a final stage kalman filter process that may reduce the noise of inaccurate location information by recursively combining and estimating the combination of location information from different sources to produce a final location estimate.

The various embodiments may be implemented on a variety of mobile computing devices, an example of which is illustrated in FIG. 19. Specifically, FIG. 19 is a system block diagram of a mobile transceiver device in the form of a smartphone/cell phone 1900 suitable for use with any of the embodiments. The cell phone 1900 may include a processor 1901 coupled to internal memory 1902, a display 1903, and to a speaker 1954. Additionally, the cell phone 1900 may include an antenna 1904 for sending and receiving electromagnetic radiation that may be connected to a wireless data link and/or cellular telephone transceiver 1905 coupled to the processor 1901. Cell phones 1900 typically also include menu selection buttons or rocker switches 1908 for receiving user inputs.

A typical cell phone 1900 also includes a sound encoding/decoding (CODEC) circuit 1924 which digitizes sound received from a microphone into data packets suitable for wireless transmission and decodes received sound data packets to generate analog signals that are provided to the speaker 1954 to generate sound. Also, one or more of the processor 1901, wireless transceiver 1905 and CODEC 1924 may include a digital signal processor (DSP) circuit (not shown separately). The cell phone 1900 may further include a peanut or a ZigBee transceiver (i.e., an IEEE 802.15.4 transceiver) 1913 for low-power short-range communications between wireless devices, or other similar communication circuitry (e.g., circuitry implementing the Bluetooth® or WiFi protocols, etc.).

Various embodiments may be implemented on any of a variety of commercially available server devices, such as the server 2100 illustrated in FIG. 20. Such a server 2000 typically includes one or more processors 2001, 2002 coupled to volatile memory 2003 and a large capacity nonvolatile memory, such as a disk drive 2004. The server 2000 may also include a floppy disc drive, compact disc (CD) or DVD disc drive 2006 coupled to the processor 2001. The server 2000 may also include network access ports 2006 coupled to the processor 2001 for establishing data connections with a network 2005, such as a local area network coupled to other communication system computers and servers.

The processors 1901, 2001, and 2002 may be any programmable microprocessor, microcomputer or multiple processor chip or chips that can be configured by software instructions (applications) to perform a variety of functions, including the functions of the various embodiments described below. In some mobile devices, multicore processors 2002 may be provided, such as one processor core dedicated to wireless communication functions and one processor core dedicated to running other applications. Typically, software applications may be stored in the internal memory 1902, 2003, and 2004 before they are accessed and loaded into the processor 1901, 2001, and 2002. The processor 1901, 2001, and 2002 may include internal memory sufficient to store the application software instructions.

The wireless device location determination techniques described herein may be implemented in conjunction with various wireless communication networks such as a wireless wide area network (WWAN), a wireless local area network (WLAN), a wireless personal area network (WPAN), and so on. The term “network” and “system” are often used interchangeably. A WWAN may be a Code Division Multiple Access (CDMA) network, a Frequency Division Multiple Access (FDMA) network, a Time Division Multiple Access (TDMA) network, an OFDMA network, a 3GPP LTE network, a WiMAX (IEEE 802.16) network, and so on. A CDMA network may implement one or more radio access technologies (RATs) such as CDMA2000, Wideband-CDMA (W-CDMA), and so on. CDMA2000 includes IS-95, IS-2000, and IS-856 standards. W-CDMA is described in documents from a consortium named “3rd Generation Partnership Project” (3GPP). CDMA2000 is described in documents from a consortium named “3rd Generation Partnership Project 2” (3GPP2). 3GPP and 3GPP2 documents are publicly available. A WLAN may be an IEEE 802.11x network, and a WPAN may be a Bluetooth network, an IEEE 802.15x, or some other type of network. The techniques may also be implemented in conjunction with any combination of WWAN, WLAN, and/or WPAN.

The various embodiments may include enhancements to the current location based service methodologies used for wireless mobile communications. Determining the location of the mobile device in a wireless network is becoming more and more important in recent years both for commercial and public safety positioning applications. Services and applications based on accurate knowledge of the location of a mobile device are becoming more prevalent in the current and future wireless communication systems Additionally Public Safety is also embarking on the use of commercial cellular technology, LTE, as a communication protocol of choice. Of specific importance is the need for improved situation awareness at an incident with first responders.

Presently GPS provides a good estimate of the mobile devices current location under optimum conditions. However in many situations and especially in building and urban environments the ability to utilize GPS for position location determination is hampered and many times is not usable. The network based solutions for determining the mobile devices location, while good, has many problems with locating the mobile device within buildings and in urban areas. The introduction of wireless network systems such as the third generation partnership project (3GPP) long-term evolution (LTE) present new capabilities has the ability in the public safety band to provide excellent coverage in urban and indoor environments. Although the wireless mobile networks can provide coverage in urban and in-building environments the location information position accuracy has limitations.

Better positional location accuracy and confidence has many advantages for use in emergency location services, commercial location services, internal location services and lawful intercept location services. The various embodiments provide the ability to improve the positional location information for both new and existing wireless networks.

For commercial applications the ability to have the mobile device improve location specific information within a multiple story building, in an urban environment or within a mall provides both network radio resource improvements and has unique advertising targeting capabilities as well as applications for improved fleet management, asset tracking and various machine to machine communications applications where positional determination is required to be highly accurate. For commercial users the need for improves position location information accuracy is most needed for in-building environments where the location of the mobile device can be more accurately pin pointed for location based services

The advantage of law enforcement with improved positional information will enable the tracking of mobile devices inside a building to enable determination of what floor or part of the building the device is being used is located without the need for replacing radio beacons or location aware access points.

For emergency services the advantage comes to better positional location of the part in need of assistance especially in an urban environment where the positional information is most problematic with existing techniques.

For first responders this enhancement enables mobile devices which are in the same scene to help augment their position coordinates with each other in a controlled ad-hoc environment. The positional information shared not only includes latitude and longitude but also altitude and velocity. Since this information involves a small amount of data the mobile devices can have the E-SMLC in the case of LTE share the information both on net and off-net.

The use of sensors including accelerometers, gyroscopes, magnetometers and pressure sensors along with GPS receivers with mobile devices is becoming more prevalent. Therefore the enhancements for positional location will give the E-SMLC in the case of LTE the ability to not only utilize GPS or Network derived coordinate information but also to have an augmentation with sensors associated the mobile device which can include accelerometers, gyroscopes, magnetometer and pressure sensors for refining and reducing some of the positional uncertainties that are in inherent to wireless positional determination.

Wireless mobile network like LTE the position location information accuracy needs to be improved for in building environments in addition to providing more accurate location information about where the mobile devices are actually located. Whether the mobile device is used by a first responder, commercial cellular user or a combination of both.

Positional location improvement enable improved situation awareness, improved telemetry, and improved overall communication with the incident commander. In addition, the mobile devices proximity location to other mobile devices can and will change dynamically allowing for resources to be added and/or reassigned as the need arises for operational requirements.

As discussed above, the various embodiments include methods, and mobile devices configured to implement the methods, of determining the location of a mobile device and providing improved location based services via the mobile device. A mobile device may be configured to perform operations that include determining an approximate location (e.g., latitude, longitude, and altitude coordinates, etc.) of the mobile device, grouping the mobile device with a wireless transceiver in proximity to the mobile device to form a communication group, sending the determined approximate location to the wireless transceiver, receiving location information from the wireless transceiver, and using the received location information to determine or compute more precise location information for the mobile device and/or to provide an enhanced location based service via the device. The operations may also include collecting information (e.g., distance, velocity, geospatial coordinates, etc.) from other devices in a communication group, computing the “more precise location information” based on the collected location information, and using the computed information (e.g., the “more precise location information” or location estimation set) to provide the enhanced location based service.

In an embodiment, the mobile device may be configured to establish communication links/connections to a plurality of other devices and/or to form a communication group in response to determining that it is not currently connected to the communications network. The device may then receive distance and/or velocity information from the other devices in the group (e.g., via the communication links/connections), use the received information to generate the “more precise location information” or a location estimation set (e.g., an information structure that includes latitude, longitude, and altitude coordinates, etc.), and use the generated information to provide an enhanced location based service. In some embodiments, the device may transmit the generated information to the other devices in the communication group. These other devices may be configured to receive and forward the information to other network resources and/or to use the received information to compute or update their location information (e.g., compute an new location estimation set, etc.).

Some of the embodiments described above (e.g., those described with reference to FIG. 16A through FIG. 18) may include, require, and/or benefit from the performance of sensor fusion operations, which generally include using information collected from internal sensors (e.g., accelerometer, gyros, magnetic compass, altimeters, odometers, etc.) of the devices to compute the “more precise” location or position information. For example, a device may be configured to locally collect information from its internal sensors, use the collected sensor data to compute movement data or a movement estimate value (e.g., for its own movement, etc.), share its movement or sensor data with other devices in the communication group, receive telemetry, sensor and/or movement data from other devices in the group, and use any combination of the locally collected, computed or received information to determine the relative movements of the devices in the group. The device may then use a combination of temporal data, relative movements of the devices in the group, and triangulation techniques to determine or compute its location, such as by computing/generating a location estimation value/set or “more precise location information” that is used to provide an enhanced location based service.

The above-described sensor fusion operations are generally every effective for computing or determining the location and position of a mobile device with sufficient precision, and with a sufficiently high degree of confidence in the accuracy of the computation (e.g., 90% confidence, 95% confidence, 99% confidence, etc.). However, previous/existing solutions may require that the communication group include a plurality (often a multitude) of devices and/or that a significant number of devices collect and report their sensor, movement or telemetry data. However, mobile devices do not always have access to a communication group (yet alone a sufficiently large communication group) and/or to the robust volume of data that could be required by these solutions. As such, it may be challenging for mobile devices to accurately determine their three-dimensional locations and positions with a sufficiently high degree of confidence (e.g., 95% confidence, 99% confidence, etc.), accuracy, and/or precision using previous/existing solutions. Various embodiments overcome these limitations of previous/existing solutions by configuring mobile devices to perform single device trilateration operations that allow the mobile devices to accurately determine their three-dimensional locations and positions with sufficiently high degrees of confidence (e.g., for its accuracy, correctness, etc.) and precision, without requiring a large number of devices be included in their communication group and without requiring that a large number of devices report their sensor, movement or telemetry data.

The various embodiments allow mobile devices to determine their three-dimensional locations and positions (e.g., via a location estimation set, etc.) with a sufficiently high degree of confidence (e.g., 95% confidence in accuracy, etc.) and precision (e.g., is highly repeatable, etc.) without requiring the devices to have access to a large communication group or a large amount of data.

A mobile device may be configured to perform advanced location based operations (e.g., advanced sensor fusion operations) to generate location information (e.g., a location estimate set/value), use a differential RMS² method (or any other method known in the art) compute confidence values, and compare the computed confidence values to one or more threshold values to determine whether there is a sufficiently high degree of confidence in the accuracy of the generated location information (e.g., location estimate set/value). In some embodiments, the mobile device may be configured to compute a confidence value between 0.0 and 1.0 that identifies a confidence level in the accuracy of the measurement for each data field in the location estimation set (e.g., a confidence value for each of the latitude, longitude and altitude data fields, etc.). For example, confidence values of 0.90, 0.95, and 0.91 may indicate that the x, y, and z coordinates are accurate within 30 meters between 90 and 95 percent of the time.

In some embodiments, the mobile device may be configured to also compute a precision value that identifies, or which is indicative of, the repeatability factor of the computation/measurements over multiple measurements. The precision value may be used to determine how often the device reports the same position/location (i.e., based on evaluating multiple reports indicating that the device has not moved more than X meters, etc.), which may be used to determine the precision of the measurement (e.g., within 1 meter, etc.). The precision value may also be used to determine the likelihood that repeating the computation (e.g., using the same inputs or input sources) will result in substantially the same values.

FIG. 21A illustrates various components, information flows, and operations in an example mobile device system 2100 configured to perform enhanced location based service (eLBS) trilateration operations in accordance with an embodiment. FIG. 21B illustrates that, in another embodiment, the mobile device system 2100 may be configured to perform single device eLBS trilateration operations that do not require receiving information from other devices in the communication group. In the examples illustrated in FIGS. 21A and 21B, the system 2100 includes a location information module 2102, a trilateration module 2104, and an output/storage module 2106.

In block 2112, a processor of the mobile device may receive information that is suitable for use as, may be used to generate, or which includes, location information, such as GPS data, a Cell ID, a WiFi ID, Beacon data, a RFID, Loran C data, OS Library Function, etc. In some embodiments, the mobile device may receive location information from active or passive external devices/systems. For example, the mobile device may communicate with an active external device, such as location based server from a fleet management company, to receive location information. As part of these operations, the mobile device may perform various operations (e.g., interrogation, etc.) to establish communication links and receive information from the active external devices. Alternatively or in addition, the mobile device may receive location information from a passive external device, such an RFID chip that scans for the presence of device and/or which broadcasts location information periodically. In addition, the mobile device may generate location information locally (in the device) based on information received from an external system in block 2112. For example, the mobile device may generate GPS data (e.g., GPS coordinates or GPS determined position information) in a local GPS receiver based on GPS information received from an external GPS system. As another example, the mobile device may use received WiFi ID information to determine or compute its proximity to known networks, and generate location information based on the determined proximity to these known networks.

In block 2114, the mobile device may generate and/or receive updated dead reckoning (DR) position information (or dead reckoning position estimate value). As mentioned above, the mobile device may be equipped with sensors (e.g., accelerometer, gyros, magnetic compass, altimeters, odometers, etc.) that allow it to estimate the distance it has traveled or moved over a period of time in any dimension (e.g., x, y or z; latitude, longitude or altitude; etc.). The mobile device may use information collected from these sensors in block 2114 to perform any or all of the dead reckoning operations discussed in this application and generate DR position information. For example, the mobile device may use information from the sensors (e.g., accelerometer, gyros, magnetic compass, altimeters, odometers, etc.) to determine the distance it has traveled (or been moved) since the last time it was able to ascertain its location with a sufficiently high degree of confidence (e.g., Cx, Cy and Cz are all greater than .95, etc.), determine its current location based on the determined distance (e.g., distance it has traveled, etc.), and generate updated DR position information that identifies its current location. In some embodiments, the mobile device may also compute confidence values and/or a precision value for the generated DR position information in block 2114.

In block 2116, the mobile device may receive and process location based service information (LBS information) from other devices, such as from transceiver or other mobile devices in the communication group. Since the LBS information may be received from devices that are being moved and/or which are not stationary, the LBS information may include, or may be used to generate or establish, multiple waypoints at discrete times and/or for discrete durations or periods of time. In some embodiments, the LBS information may include estimated distances between multiple (e.g., three or more) devices/transceivers and the mobile device. Each waypoint may be an information structure that includes one or more information fields, component vectors, location information, position information, coordinate information, etc.

Thus, the location information module 2102 of the mobile device may be configured to receive, process and/or generate standard location information (or a first data set, estimate value, etc.) in block 2112, updated DR position information (or a second data set, estimate value, etc.) in block 2114, and LBS information (a third data set, estimate value, etc.) in block 2116. In operation 2140, the location information module 2102 may send any or all such information (e.g., first, second, and third values/sets) to the trilateration module 2104 as input data.

In blocks 2118-2122, the mobile device/trilateration module 2104 may use the received input data to perform trilateration operations (e.g., trilateration API location operations, etc.), determine the geographical coordinates (e.g., latitude, longitude, and altitude coordinates) of the mobile device, generate a trilateration position estimate value, generate a final position set (e.g., a final location estimate value), generate an updated final position set (e.g., x, y and z coordinates, an updated position estimate value, more precise information, etc.), and send the updated final position set to the output/storage module 2106. The trilateration operations may include operations for implementing any or all of the techniques discussed in this application, including time of arrival, angle of arrival, mobile-to-mobile trilateration, lateration, multilateration, triangulation, etc.

In the example illustrated in FIG. 21A, in block 2118, the mobile device generates/computes/receives trilateration location values (X, Y, Z), a time value, trilateration location delta values (ΔX, ΔY, ΔZ), confidence values (C_(x),C_(y),C_(z)), and one or more precision values, the combination of which may be stored or used as a waypoint (or a data set or estimate value). In block 2120, the mobile device may rank or assign weights to the current or historical waypoints (i.e., previously computed waypoints). In block 2122, the mobile device may generate two or three dimensional vectors using the waypoints (current and/or historic). In an embodiment, the mobile device may generate the vectors based on their rank/weights (e.g., by including/using only waypoints having a rank that exceeds a threshold value).

As mentioned above, the trilateration module 2104 may send the computed updated final position set to the output/storage module 2106. The output/storage module 2106 may store the updated final position set in a location buffer or the illustrated updated final position datastore 2124. In block 2126, the output/storage module 2106 may use the updated final position set (more precise location information) to provide a location based service. In block 2128, the output/storage module 2106 may send the updated final position set to other devices, such as to a network server or the other mobile devices in the communication group.

In order to accurately compute/determine the updated final position set, the mobile device system 2100 may be required to communicate with other devices in a communication group (e.g., in block 2116). However, mobile devices do not always have access to a communication group (yet alone a sufficiently large communication group) and/or to the robust data that could be required to accurately determine the location of the device. As such, in the example illustrated in FIG. 21B, in block 2144, the mobile device may receive LBS information from a server computing device (e.g., a network provided location service). In operation 2142, the mobile device may send the standard location information (or first data set, estimate value, etc.), updated DR position information (or second data set, estimate value, etc.), and the LBS information received from the server (or third data set, estimate value, etc.) to the trilateration module 2104 as input data. The trilateration module 2104 may receive and use the input data to compute/generate a final position set and/or an updated final position set, and send the generated position set to the output/storage module 2106 for storage and/or use.

FIG. 21C illustrates various additional components, information flows, and operations in an example mobile device system 2100 configured to perform enhanced location based service (eLBS) trilateration operations in accordance with the various embodiments. In block 2152, the mobile device may use information received from active and/or passive external devices or systems to generate a first data set (e.g., x, y and z coordinates, first estimate value, etc.). In block 2154, the mobile device may use information collected from internal sensors and systems to perform dead reckoning operations and generate a second data set (e.g., x, y and z coordinates, second estimate value, etc.). In block 2156, the mobile device may receive location based service (LBS) information from a server (e.g., x, y and z coordinates, LBS estimate value, etc.). In block 2156, the mobile device may pass the received LBS information through a first kalman filter (Kalman Filter 1) to generate filtered LBS data (e.g., a filtered LBS estimate value, etc.). The kalman filter may be a procedure, algorithm, method, technique, or sequence of operations for accomplishing the function of a kalman filter.

In block 2160, the mobile device may perform trilateration operations (e.g., trilateration API location operations, etc.), determine the geographical coordinates of the mobile device, and generate a third data set (e.g., x, y and z coordinates, third estimate value, etc.) based on the determined geographical coordinates. In block 2162, the mobile device may pass the first, second, and third data sets (or estimate values, etc.) through a second kalman filter (Kalman Filter 2) to generate a position set (e.g., final position set, final location estimate value, updated final location estimate value, etc.). In block 2164, the mobile device may use the position set to determine/compute the current location of the device. As part of these operations, the mobile device may generate a waypoint information structure (or estimate value) that includes trilateration location values (X, Y, Z), a time value, trilateration location delta values (ΔX, ΔY, ΔZ), confidence values (C_(x),C_(y),C_(z)), and one or more precision values, and use the generated way point to set the current location of the device. In an embodiment, the mobile device may be configured to store the waypoint in a list (or other information structure) in conjunction with a timestamp.

FIG. 22 illustrates a method 2200 of performing enhanced location based service (eLBS) trilateration operations in accordance with an embodiment. Method 2200 may be performed by a processor in a mobile computing device. In block 2202, the processor may determine and store the current location of the mobile device, such as by performing any or all of the operations discussed in this application. For example, in block 2200, the processor may compute and use the initial position value discussed above with reference to FIG. 15. In block 2204, the processor may receive a newly reported position update (location information) from a position location source, such as an external device, an OS library function, via sensors/dead reckoning, and/or a network provided location service. In block 2206, the processor may determine or compute an updated location set (e.g., latitude, longitude and altitude coordinates, an estimate value, etc.) for the newly reported position update. For example, the processor may perform trilateration operations, using the information received from the position location source as input, to generate a new updated location set (or updated final location estimate value, etc.). In block 2208, the processor may normalize reporting times (e.g., to t=“0”). In block 2210, the processor may determine rank/weight values for the updated location set with respect to the current location and based on a confidence interval.

In determination block 2212, the processor may determine whether the updated location set is valid. In response to determining that the updated location set is not valid (i.e., determination block 2212=“No”), the processor may discard the measurement (e.g., the updated location set and/or the received information) or mark it as invalid in block 2218. In response to determining that the updated location set is valid (i.e., determination block 2212=“Yes”), in block 2214 the processor may insert the updated location set (or updated final location estimate value, etc.) into a sorted list of coordinates under a position source with a confidence interval and reporting time. In addition, the processor may retain past location determinations (e.g., previously computed waypoints, estimate values, etc.) by, for example, marking or otherwise designating them as historical waypoints. In block 2216, the processor may update a position location table to include the computed updated location set. In addition, in some embodiments, the processor may set the current location of the device to be equal to the updated location set and repeat the operations described above to continuously or repeated update its current location and generate more precise location information.

FIGS. 23 through 30 illustrate a method 2300 for performing enhanced location based service (eLBS) operations in accordance with various embodiments. Method 2300 may be performed by a processor or processing core in a mobile device. The operations illustrated in each of FIGS. 23-29 may result in the generation and storage of a temporary location value or waypoint (e.g., a temporary GPS waypoint, temporary Loran C waypoint, etc.) that is used to compute more precise location information in the mobile device (e.g., via the operations illustrated in FIG. 30). Further, the operations illustrated in FIGS. 23-29 may be performed concurrently, in parallel, or in conjunction with one another to generate multiple temporary location values/waypoints, any or all of which may be selected and used to compute more precise location information (e.g., via the operations illustrated in FIG. 30).

In some embodiments (e.g., the embodiments discussed with reference to FIGS. 23-29), the mobile device may be configured to report location information (e.g., GPS location/position updates, etc.). The mobile device may report location information by generating and sending data, location estimate values, location sets, waypoints, and other location-related information and/or information structures/units to a server and/or other mobile devices, such as in response to receiving location information and/or in response to determining that the received/retrieved location information may be used to obtain an adequate positional fix. As such, the reporting of location information by the mobile device may be indicative of its ability to acquire/obtain an adequate positional fix.

As mentioned above, the mobile device may be configured to generate and use waypoints. A waypoint may be an information structure that includes one or more information fields, component vectors, location information, position information, coordinate information, etc. For example, a waypoint may be an information structure that includes latitude, longitude and altitude coordinates, as well as a confidence value, precision value, rank, a time stamp, and an information type identifier (e.g., GPS, Loran C, sensor, combined, etc.). A waypoint may also be, or may include, a location estimate value, a location set, or any other similar location information suitable for adequately conveying or communicating location information.

A “waypoint list” may be a an information structure that includes one or more waypoints. A waypoint list may be ordered list in which a waypoint's position in the list indicates each waypoint's rank, when its corresponding location information was received, when the information was reported, etc. In some embodiments, the waypoint list may be a stack. In some embodiments, the waypoint list may include waypoints of a single information type. In some embodiments, the system may include multiple waypoint lists (GPS waypoint list, Loran C waypoint list, reported waypoint list, reported GPS waypoint list, final location waypoint list, updated final location waypoint list, etc.).

With reference to FIG. 23, in block 2302, the processor may poll or monitor sensors, communications circuitry, data ports, buses, etc. to receive or retrieve location information, such as GPS data, Loran C, Cell ID, etc. In the example illustrated in FIG. 23, in block 2302, the processor monitors GPS circuitry to receive GPS data from a GPS system. In some embodiments, the processor may use the received information to generate a “working” or current waypoint in block 2302.

In determination block 2304, the processor may determine whether the device is reporting location information so as to determine whether the system was able to obtain an adequate positional fix using the received GPS data. The processor may determine whether the mobile device is reporting location information by retrieving, obtaining, examining, or accessing a list of reported locations/waypoints. For example, the processor may compare or evaluate timestamps that are associated with recently reported GPS waypoints (e.g., reported waypoints having an information type identifier of “GPS”) that are stored in a list of reported GPS waypoints (reported GPS waypoint list, location list, etc.) to determine whether the device's location was reported after the GPS data was received (in which case, the device is reporting the location). Similarly, the processor may determine whether the device's location was reported within a certain time period (e.g., within the past 2 seconds, 10 seconds, 1 minute, 10 minutes, etc.), which may be a variable or preset value that is determined/selected dynamically based on the location based service(s) being provided by the device.

In response to determining that the device is not reporting location information (i.e., determination block 2304=“No”), the processor may discard or disregarded the received GPS data (or the working/current waypoint), and select a location\waypoint (or location set, location estimate value, etc.) from memory in block 2306. This may be accomplished by selecting a waypoint from a reported waypoint list (e.g., a list of reported, trusted, or historical waypoints, etc.) that is stored in the local memory of the device. In some embodiments, in block 2306, the processor may select a prior reported waypoint (e.g., t−1 or t−2, where t=0 is the current location in time) having the highest confidence/rank, and update (or correct the range of) the selected waypoint to more accurately represent the current location and position of the device. That is, the processor may update the selected waypoint for t=0 (where t=0 is the current location in time), which may be accomplished using any or all of the location update methods, procedures, or solutions discussed in this application. If there are no previously reported waypoints stored in the memory, or if the processor determines that the stored waypoints have low confidence values (i.e., a value that is less than a threshold value), in block 2306 the processor may select a null value so as to indicate that an adequate location/position is not available.

In response to determining that the device is reporting location information (i.e., determination block 2304=“Yes”), in determination block 2308, the processor may determine whether the confidence or rank of the current/working waypoint (e.g., last GPS waypoint that was generated and reported, which represents the received GPS data) is greater than or equal to the confidence/rank of one or more of the other previously reported waypoints. For example, the processor may traverse a list of reported GPS waypoints and determine whether a confidence value associated with the current/working waypoint (or the most recently reported GPS waypoint or the “t−0 waypoint”) is greater than or equal to the confidence value associated with the second most recently reported GPS waypoint (e.g., the “t−1 waypoint”). As another example, the processor may determine whether the confidence value associated with the working/current waypoint is greater than or equal to the confidence values associated with the second and third most recently reported GPS waypoints (e.g., the t−1 or t−2 waypoints, etc.), is greater than or equal to the confidence values of the GPS waypoints that were reported within a period of time (e.g., within the past 10 min), etc.

In response to determining that the confidence/rank of the current/working waypoint is less than (i.e., is not greater than or equal to) the confidence/rank of the previously reported locations/waypoints (i.e., determination block 2308=“No”), the processor may select one of the previously reported locations/waypoints (e.g., t−1 or t−2 waypoints, etc.) from memory in block 2306. On the other hand, in response to determining that the confidence or rank of the current/working waypoint is greater than or equal to the confidence or rank of the previously reported locations/waypoints (i.e., determination block 2308=“Yes”), in block 2310 the processor may select the current/working waypoint.

In block 2312, the processor may perform a GPS location update based on the selected waypoint (e.g., the current/working waypoint, a previously reported location/waypoint, etc.) and/or store the selected waypoint in memory. For example, the processor may generate a final GPS position estimate value and/or final/updated GPS waypoint based on the selected waypoint. In some embodiments, the processor may generate and set a temporary GPS waypoint based on (or to be equal to) the final/updated waypoint or final GPS position estimate value. The temporary GPS waypoint may be used as an estimate of the device current location. In block 2314, the processor may synchronize the stored or temporary GPS waypoint of the device with a time stamp (e.g., for t=0) and/or extrapolate the information to fit a current time, and update or set the temporary GPS waypoint based on the results of the synchronizing/extrapolating operations. After the completion of the operations of block 2314, the memory stores a temporary GPS waypoint that is representative of the device's current location (based on GPS data).

FIGS. 24-29 illustrate that the same or similar operations as those discussed above with reference to FIG. 23 may be performed for the different data types to generate a plurality of temporary waypoints, any or all of which may be used to determine the precise location, or “more precise location,” of the mobile device.

With reference to FIG. 24, in block 2402, the processor may receive location information in the form of Loran C data (similar to the operations in block 2302) and generate a current or working waypoint. In determination block 2404, the processor may determine whether the mobile device is reporting location information (e.g., by accessing a list of reported locations, a Loran C waypoint list, etc.) so as to determine whether the system was able to obtain an adequate location/positional fix based on the Loran C data. In response to determining that the device is not reporting location information (i.e., determination block 2404=“No”), the processor may discard/disregard the working/current waypoint (and thus the received Loran C data) and select a location/waypoint from memory in block 2406. For example, the processor may select a stored waypoint (a previously reported location/waypoint) having the highest confidence/rank (t−1 or t−2). In addition, in block 2406, the processor may update the selected waypoint for t=0 (i.e., the current location in time) to more accurately represent the current location and position of the device. Also in block 2406, if there are no previous locations/waypoints stored in the memory, or the stored locations/waypoints have low confidence values, the processor may select a null value to indicate that an adequate location/position is not available (based on the Loran C data).

In response to determining that the device is reporting location information (i.e., determination block 2404=“Yes”), in determination block 2408, the processor may determine whether the confidence/rank of the working/current waypoint is greater than or equal to the confidence/rank of a previously reported location/waypoint (similar to block 2308). In response to determining that the confidence or rank of the working/current waypoint (i.e., last reported location/waypoint) is less than the confidence or rank of the previously reported location/waypoint (i.e., determination block 2408=“No”), in block 2406 the processor may discard/disregard the working/current waypoint (and thus the received Loran C data), and select a previously computed location/waypoint (e.g., a “Loran C” waypoint, which is a waypoint having an information type identifier of “Loran C”) from memory.

In response to determining that the confidence or rank of the working/current waypoint (or last reported location/waypoint) is greater than or equal to the confidence or rank of the previously reported location/waypoint (i.e., determination block 2408=“Yes”), in block 2410 the processor may select the working/current waypoint (or most recently reported location/waypoint). In block 2412, the processor may perform a loran C location update and generate a final position estimate value (or an updated/final waypoint) based on the selected waypoint. The processor may also generate, update or set the temporary Loran C waypoint based on the final position estimate value or updated/final waypoint. In block 2414, the processor may synchronize the temporary Loran C waypoint with a time stamp (e.g., for t=0) and/or extrapolate the information to fit the current time. As such, after the completion of the operations of block 2414, the memory stores an updated temporary Loran C waypoint, which represents a current location of the device based on Loran C data.

In some embodiments, the operations illustrated in FIG. 23 may be performed in conjunction with the operations illustrated in FIG. 24. For example, the system could be configured to generate a temporary GPS waypoint and a temporary Loran C waypoint, each of which represents a current location of the device. As is discussed further below with reference to FIG. 30, the mobile device processor may use each of these waypoints as inputs to kalman filter, the output of which may be used to generate a single final waypoint (or an updated final position estimate value, more precise location information, etc.) that identifies the precise location of the device.

With reference to FIG. 25, in block 2502, the processor may receive location information in the form of Cell IDs and generate a working/current waypoint. In determination block 2504, the processor may determine whether it is reporting the location information. In response to determining that the device is not reporting location information (i.e., determination block 2504=“No”), in block 2506, the processor may select location/waypoint having the highest confidence/rank (t−1 or t−2) from memory, and update (or correct) the selected location/waypoint to more accurately represent the current location and position of the device (similar to the operations in blocks 2306 and 2406 discussed above). If there are no previous locations/waypoints stored in the memory, or the stored locations/waypoints have a low confidence value (i.e., a value that is less than a threshold value), the processor may select a null value to indicate that an adequate location/position is not available in block 2506. In addition, in block 2506, the processor may obtain bearing and DR distance information for/from the available sensors (if such information is available).

In response to determining that the device is reporting location information (i.e., determination block 2504=“Yes”), in determination block 2508, the processor may determine whether the confidence or rank of the working/current waypoint (or last reported location/waypoint) is greater than or equal to the confidence/rank of a previously reported location/waypoint (similar to block 2308). In response to determining that the confidence/rank of the working/current waypoint is not greater than or equal to the confidence or rank of the previously reported location/waypoint (i.e., determination block 2508=“No”), the processor may select a previously computed location/waypoint from memory in block 2506 (similar to block 2306). In response to determining that the confidence or rank of the working/current waypoint is greater than or equal to the confidence or rank of the previously reported location/waypoint (i.e., determination block 2508=“Yes”), in block 2510 the processor may select the working/current waypoint (most recently reported location/waypoint).

In block 2512, the processor may perform a Cell ID update based on the selected waypoint. For example, the processor may generate a final position estimate value or an updated Cell ID waypoint based on the selected waypoint/value/location (e.g., the current/working waypoint, previously reported locations/waypoints, etc.). In some embodiments, the processor may generate and set a temporary Cell ID waypoint based on (or to be equal to) the updated Cell ID waypoint or final position estimate value. The temporary Cell ID waypoint may be used as an estimate of the device current location. In block 2514, the processor may synchronize the temporary Cell ID waypoint with a time stamp (e.g., for t=0) and/or extrapolate the information to fit a current time. As such, after the completion of the operations of block 2514, the memory stores a temporary Cell ID waypoint that is representative of the device's current location.

Thus, the processor may determine that the device is not able to obtain a positional fix, and therefore it is not reporting the positional information, and therefore no CellID position update or positional information has been obtained. When there is no CellID positional data being reported, previous CellID positional locations that were stored may be used in place of the expected CellID positional location data. The highest rank/confidence location position may be used, and this historic position may be position corrected for the t=0 where t=0 is the device's current location in time. If there are no previous positions stored in memory, or the positions stored are below a defined confidence value then a null value, no position available may be indicated. If the CellID position location reported has a lower confidence than the previous position location reported (i.e., determination block 2508=“No”), then the current position location data is passed to block 2506 and/or a previously computed waypoint is selected. If the CellID position location reported is at the same or higher confidence than the previous position location (i.e., determination block 2508=“Yes”) then this position is listed as position t=0 in block 2510, and sent to process 2512 for the CellID location update The position location update may also be stored in a database used for potential use in block 2406 for future position updates.

In some embodiments, the operations illustrated in FIG. 25 may be performed in conjunction with the operations illustrated in FIGS. 23 and 24. For example, the system could be configured to generate a temporary Cell ID waypoint, a temporary Loran C waypoint, and a temporary GPS waypoint (serially or in parlledl), and each of these waypoints may independently represent the current location of the device.

With reference to FIG. 26, in block 2602, the processor may poll or monitor sensors or communications circuitry to receive location information in the form of WIFI IDs, and generate a working/current waypoint. In determination block 2604, the processor may determine whether it is reporting location information (i.e., to determine whether a WIFI ID position update or positional information is obtained). In response to determining that the device is not reporting location information (i.e., determination block 2604=“No”), the processor may select a location/waypoint having the highest confidence/rank (t−1 or t−2) from memory, and update (or correct the range of) the selected location/waypoint to more accurately represent the current location and position of the device in block 2606. Also in block 2606, if there are no previous locations/waypoints stored in the memory, or the stored locations/waypoints have low confidence values (i.e., a value that is less than a threshold value), the processor may select a null value to indicate that an adequate location/position is not available. In addition, in block 2606, the processor may obtain bearing and DR distance information for sensors (if available).

Thus, in block 2604 the processor may determine that WIFI ID is not able to obtain a positional fix, and therefore it is not reporting the positional information, and therefore no WIFI ID position update or positional information is obtained. In block 2606, when there is no WIFI ID positional data being reported, previous WIFI ID positional locations that were stored are used in place of the expected WIFI ID positional location data. The highest rank/confidence location position (waypoint) may be used, and it historic position may be position corrected for the t=0 (where t=0 is the current location in time). If there are no previous positions (waypoints) are stored, or the positions (waypoints) stored are below a defined confidence value, then a null value or no position available may be indicated.

In response to determining that the device is reporting location information (i.e., determination block 2604=“Yes”), in determination block 2608, the processor may determine whether the confidence/rank of the working/current waypoint (last reported location/waypoint) is greater than or equal to the confidence/rank of a previously reported location/waypoint (similar to blocks 2308, 2408, 2508, etc.). In response to determining that the confidence or rank of the working/current waypoint is not greater than or equal to the confidence or rank of the previously reported location/waypoint (i.e., determination block 2608=“No”), in block 2606 the processor may select a previously computed location/waypoint from memory. In response to determining that the confidence or rank of the working/current waypoint is greater than or equal to the confidence or rank of the previously reported locations/waypoints (i.e., determination block 2608=“Yes”), in block 2610 the processor may select the working/current waypoint (the most recently reported location/waypoint).

In block 2612, the processor may perform a WIFI ID location update based on the selected waypoint. For example, the processor may generate a final position estimate value or an updated WIFI ID waypoint based on the selected value/waypoint (e.g., the current/working waypoint, previously reported locations/waypoints, etc.). In some embodiments, the processor may generate and set a temporary WIFI ID waypoint based on (or to be equal to) the updated WIFI ID waypoint or final position estimate value. The temporary WIFI ID waypoint may be used as an estimate of the device current location. In block 2614, the processor may synchronize the temporary WIFI ID waypoint with a time stamp (e.g., for t=0) and/or extrapolate the information to fit a current time. As such, after the completion of the operations of block 2614, the memory stores a temporary WIFI ID waypoint that is representative of the device's current location. working/current waypoint

Thus, in block 2608, if the WiFiID position location reported has a lower confidence than the previous position location reported then the current position location data may be passed to block 2606. If the WiFiID position location reported is at the same or higher confidence than the previous position location, then this position is listed as position t=0 in block 2610, and sent to block 2612 for the WiFiID location update The position location update is also stored in the database used for potential use in block 2606 for future position updates.

In some embodiments, the operations illustrated in FIG. 26 may be performed in conjunction with the operations illustrated in FIGS. 23 through 25. For example, the system could be configured to generate a temporary WiFiID waypoint, temporary CellID waypoint, a temporary Loran C waypoint, and a temporary GPS waypoint each of which represents a current location of the device.

With reference to FIG. 27, in block 2702, the processor may poll or monitor sensors or communications circuitry to receive location information in the form of Beacons, RFIDs or other similar information from other similar location devices, and generate a current/working waypoint (similar to block 2302). In determination block 2704, the processor may determine whether it is reporting location information (similar to block 2304). In response to determining that the device is not reporting location information (i.e., determination block 2704=“No”), the processor may select a location/waypoint having the highest confidence/rank (t-1 or t−2), and update (or correct the range of) the selected location/waypoint to more accurately represent the current location and position of the device in block 2706. Also in block 2706, if there are no previous waypoints stored in the memory, or the stored waypoints have a low confidence value (i.e., a value that is less than a threshold value), the processor may select a null value to indicate that an adequate location/position is not available. In addition, in block 2706, the processor may obtain bearing and DR distance information for sensors (if available).

In response to determining that it is reporting location information (i.e., determination block 2704=“Yes”), in determination block 2708, the processor may determine whether the confidence or rank of the current/working waypoint is greater than or equal to the confidence/rank of a previously reported waypoint (similar to block 2308). In response to determining that the confidence or rank of the current/working waypoint is not greater than or equal to the confidence or rank of the previously reported waypoint (i.e., determination block 2708=“No”), in block 2706 the processor may select a previously computed waypoint from memory. In response to determining that the confidence or rank of the current/working waypoint is greater than or equal to the confidence or rank of the previously reported waypoint (i.e., determination block 2708=“Yes”), in block 2710 the processor may select the current/working waypoint. In block 2712, the processor may perform a (Beacon, RFID, etc.) location update and generate a final position estimate value (or an updated final position estimate value) based on the selected waypoint. In block 2714, the processor may synchronize the current/working waypoint (similar to block 2314). After the completion of the operations of block 2714, the memory stores a temporary waypoint (temporary Beacon waypoint, temporary RFID waypoint, etc.) that is representative of the device's current location.

Said another way, in block 2704 the processor determines that Beacon/RFID is not able to obtain a positional fix and therefore it is not reporting the positional information and therefore no Beacon/RFID position update or positional information is obtained. In block 2306, when there is no Beacon/RFID positional data being reported, previous Beacon/RFID positional locations that were stored are used in place of the expected Beacon/RFID positional location data. The highest rank, ie confidence, location position is used and this historic position is position corrected for the t=0 where t=0 is the current location in time. If there are no previous positions stored or the positions stored are below a defined confidence value then a null value, no position available indicated. In block 2708 if the Beacon/RFID position location reported has a lower confidence than the previous position location reported, then the current position location data is passed to bock 2706. If the Beacon/RFID position location reported is at the same or higher confidence than the previous position location, then this position is listed as position t=0 in block 2710, and sent to process 2712 for the Beacon/RFID location update. The position location update is also stored in the database used for potential use in block 2706 for future position updates.

In some embodiments, the operations illustrated in FIG. 27 may be performed in conjunction with the operations illustrated in FIGS. 23 through 26. For example, the system could be configured to generate a temporary RFID waypoint, a temporary WiFiID waypoint, temporary Ce11ID waypoint, a temporary Loran C waypoint, and a temporary GPS waypoint each of which independently represents a current location of the device.

With reference to FIG. 28, in determination block 2802, the processor may determine whether dead reckoning is available on this device. In response to determining that dead reckoning is available (i.e., determination block 2802=“Yes”), the processor may determine whether it is reporting the information in determination block 2804. In response to determining that the device is not reporting information (i.e., determination block 2804=“No”), the processor may select a location/waypoint having the highest confidence/rank (t−1 or t−2), and update (or correct the range of) the selected location/waypoint to more accurately represent the current location and position of the device in block 2806. Also in block 2806, if there are no previous locations/waypoints stored in the memory, or the stored locations/waypoints have a low confidence value (i.e., a value that is less than a threshold value), the processor may select a null value to indicate that an adequate location/position is not available.

In response to determining that the mobile device is reporting location information (i.e., determination block 2804=“Yes”), in determination block 2808, the processor may determine whether the confidence or rank of the last reported location/waypoint (or a working/current waypoint) is greater than or equal to the confidence/rank of a previously reported location/waypoint (similar to block 2308). In response to determining that the confidence or rank of the last reported location/waypoint is not greater than or equal to the confidence or rank of the previously reported location/waypoint (i.e., determination block 2808=“No”), in block 2806 the processor may select a previously computed location/waypoint from memory. In response to determining that the confidence or rank of the last reported waypoint is greater than or equal to the confidence or rank of the previously reported waypoint (i.e., determination block 2808=“Yes”), in block 2810 the processor may select the reported waypoint (similar to block 2310). In block 2812, the processor may perform a dead reckoning position update based on the selected waypoint/value/location. For example, the processor may generate a final position estimate value (or an updated waypoint) based on the last reported location/waypoint, current/working waypoint, previously reported locations/waypoints, etc. The processor may also set the temporary current location of the device based on the final position estimate value or updated waypoint, and generate a temporary DR waypoint that is representative of the device's current location (based on DR information).

Said another way, in block 2802, the processor may determine whether dead reckoning is available on this device. If dead reckoning is not available, in block 2806, the processor provides a null value for the dead reckoning update (e.g., by sending the null value to the dead reckoning module of the device). However, if dead reckoning is available with the device, in block 2804, the processor determines if the dead reckoning capability is being reported. Dead reckoning may not be reported if there is a sensor problem on the handset. If there is a dead reckoning position reporting problem, in block 2806, the processor selects a previous dead reckoning position, which is corrected for the current time based on previous trajectory calculations for an axis (or two or three axes) that is/are available. In block 2806 when there is no read reckoning positional data being reported, previous dead reckoning positional locations (that were previously determined and stored in memory) may be used in place of the expected dead reckoning positional location data. The highest rank/confidence location position (waypoint) is used and this historic position is position corrected for the t=0 where t=0 is the current location in time. If there are no previous positions stored or the positions stored are below a defined confidence value then a null value, no position available indicated. In block 2808, if the dead reckoning position location reported has a lower confidence than the previous position location reported, then the current position location data may be passed to block 2806. If the dead reckoning position location reported is at the same or higher confidence than the previous position location then this position is listed as position t=0 in block 2810, and sent to block 2812 for the dead reckoning location update. The position location update is also stored in the database used for potential use in block 2806 for future position updates.

In some embodiments, the operations illustrated in FIG. 28 may be performed in conjunction with the operations illustrated in FIGS. 23 through 27. For example, the system could be configured to generate a temporary DR waypoint, temporary RFID waypoint, a temporary WiFiID waypoint, a temporary CellID waypoint, a temporary Loran C waypoint, and a temporary GPS waypoint each of which independently represents a current location of the device.

With reference to FIG. 29, in block 2902, the processor may receive network provided location information from a server computing device, and generate a working/current waypoint based on this information. In determination block 2904, the processor may determine whether it is reporting location information in response to receiving the network provided location information from the server computing device. In response to determining that the device is not reporting location information (i.e., determination block 2904=“No”), the processor may select a location/waypoint having the highest confidence/rank (t−1 or t−2) from memory, and update (or correct the range of) the selected waypoint to more accurately represent the current location and position of the device in block 2906. Also in block 2906, if there are no previous waypoints stored in the memory, or the stored waypoints have a low confidence value (i.e., a value that is less than a threshold value), the processor may select a null value to indicate that an adequate location/position is not available. In addition, in block 2906, the processor may obtain bearing and DR distance information for sensors (if available).

In response to determining that it is reporting location information (i.e., determination block 2904=“Yes”), in determination block 2908, the processor may determine whether the confidence or rank of the working/current waypoint (or last reported waypoint) is greater than or equal to the confidence/rank of a previously reported waypoint. In response to determining that the confidence or rank of the working/current waypoint is not greater than or equal to the confidence or rank of the previously reported waypoint (i.e., determination block 2908=“No”), in block 2906 the processor may select a previously computed waypoint from memory. In response to determining that the confidence or rank of the working/current waypoint is greater than or equal to the confidence or rank of the previously reported waypoint (i.e., determination block 2908=“Yes”), in block 2910 the processor may select the working/current waypoint.

In block 2912, the processor may perform a location update based on the selected waypoint, generate a final position estimate value or an updated network location waypoint, generate and set a temporary network-location-based waypoint based on (or to be equal to) the updated network location waypoint or final position estimate value, etc. The processor may also set the temporary current location of the device based on the updated waypoint or generated final position estimate value . In block 2914, the processor may synchronize the current location/position of the device and/or extrapolate the information to fit a current time. After the completion of the operations of block 2914, the memory stores a temporary network-location-based waypoint that is representative of the device's current location.

Thus, for the network provided location position update, the device's position/location information is calculated in a server and reported back to the device. In block 2904 the processor determines that it is not able to obtain a positional fix based on this “network provided location position” information because it is not reporting the positional information, and thus no network provided location position update or positional information is obtained. In block 2906 when there is no “network provided location position” positional data being reported previous network provided location position positional locations that were stored are used in place of the expected network provided location position positional location data. The highest rank, ie confidence, location position is used and this historic position is position corrected for the t=0 where t=0 is the current location in time. If there are no previous positions stored or the positions stored are below a defined confidence value then a null value, no position available indicated. In block 2908 if the network provided location position reported has a lower confidence than the previous position location reported then the current position location data is passed to block 2906. If the network provided location position reported is at the same or higher confidence than the previous position location then this position is listed as position t=0 , block 2310, and sent to block 2312 for the Network Provided Location Position location update The position location update is also stored in the database used for potential use in block 2306 for future position updates.

In some embodiments, the operations illustrated in FIG. 29 may be performed in conjunction with the operations illustrated in FIGS. 23 through 28. For example, the system could be configured to generate a temporary network-location-based waypoint, a temporary DR waypoint, temporary RFID waypoint, a temporary WiFiID waypoint, a temporary CellID waypoint, a temporary Loran C waypoint, and a temporary GPS waypoint, each of which may independently represent a current location of the device.

With reference to FIG. 30, in blocks 3002 to 3018, the processor may receive any or all of the updated location/position information discussed above (e.g., temporary network-location-based waypoint, a temporary DR waypoint, temporary RFID waypoint, a temporary WiFiID waypoint, a temporary CellID waypoint, a temporary Loran C waypoint, and a temporary GPS waypoint) and perform various operations to identify and select the best available information. The processor may also use the selected information to compute more precise location information (e.g., an updated final waypoint, updated final position estimate value, etc.). For example, the processor may select the top three or four positions/waypoints that are reported. If there are not enough inputs available, the processor may select and use the best of what is reported/available until it determines or identifies three or four valid inputs. A further update may not occur until the position of the device has changed, either by a difference in the current position verse the last position update or a time has expired, or if there was a change in dead reckoning or with one of the externally provided position sources.

In particular, in block 3002, the processor may determine the number of valid values available (e.g., based on the reported information, temporary current locations, waypoints, final position estimate values, etc.). For example, the processor may select one waypoint from each of a plurality of waypoint lists (e.g., a temporary GPS waypoint list, a temporary network-location-based waypoint list, a temporary DR waypoint list, etc.), evaluate each of the selected waypoints to determine whether they store a valid location value (e.g., are not equal to null, were recently reported, etc.), and increment a counter for each waypoint that is determined to store a valid location.

In determination block 3004, the processor may determine whether the number of valid location positions (counter value) is less than a threshold value (three in the example illustrated in FIG. 30). In response to determining that the number of valid location positions available (counter value) is less than the threshold value (i.e., determination block 3004=“Yes”), in block 3006 the processor may select from memory a location or waypoint having the highest rank or confidence value, and generate a final waypoint based on the selected location/waypoint. For example, the processor may select a waypoint from a list of previously reported final waypoints stored in memory. In response to determining that the number of valid location positions is not less than the threshold (i.e., determination block 3004=“No”), in block 3008 the processor may select the two or more waypoints having the highest confidence values as filter input. In block 3010, the processor may apply the selected filter input to the kalman filer (e.g., a module that implements a procedure, algorithm, method, technique, or sequence of operations for accomplishing the function of a kalman filter) to generate output identifying a single location/position (e.g., a single estimate of position, velocity, and acceleration, etc.), use the output to generate a final waypoint.

In block 3012, the processor may obtain, compute, or generate a revised or updated position location (e.g., by generating an updated final position estimate value, etc.) based on the final waypoint (generated in block 3006 or 3010), generate an updated final waypoint, and store the updated final waypoint in memory as the current location (e.g., at t=0). In determination block 3014, the processor may compare the updated final waypoint to previously computed/stored updated final waypoint (which may be stored in an updated final waypoint list) to determine whether the location of the device has changed in any axis by an amount that exceeds (e.g., is greater than or equal) a threshold amount. Said another way, the processor may determine whether the updated location information differs from the previously computed location information by x % or more in any axis in determination block 3014.

In response to determining that the location of the device has changed (in any axis) by an amount that exceeds the threshold amount (i.e., determination block 3014=“Yes”), the processor may recalculate or recomputed the location/position of the device in block 3016 (such as by performing any or all of the operations in FIGS. 23-30). In response to determining the location of the device has not changed (in any axis) by an amount that exceeds the threshold amount (i.e., determination block 3014=“No”), in block 3018 the processor may wait for an “X” amount of time or until changes are reported by external LBS inputs, network LBS or Dead Reckoning before repeating any or all of the operations discussed above with reference to FIGS. 23-30. That is, if the location of the device has not changed (in any axis) by an amount that exceeds the threshold amount, then no further updates are initiated until either a configurable time has expired or there has been an change in LoranC, GPS, WiFi ID, Beacons, Cell ID, Dead Reckoning, etc.

The various enhanced location based service (eLBS) methods discussed in this application (e.g., method 2300, etc.) may be implemented in a mobile computing device as part of a eLBS module, which may be used by client applications of the device to provide location based services. The mobile device may include a High Level Operating System (HLOS), and the eLBS module may be implemented on the device such that it may use or access the other sensor and communications modules in that device as via system or function calls to the HLOS. For example, the eLBS module may utilize the HLOS and/or the features that are available or accessible via the HLOS to collect information (e.g., location-based information, sensor information, etc.), and use this information to perform its various eLBS operations.

FIG. 31 illustrates an example mobile device 102 that includes an eLBS module 3120 and an operating system 3118 suitable for executing software applications (e.g., App1, App2, App3, App4—eLBS, etc.), which is coupled to a compass 3102, a magnetometer 3104, a gyroscope 3106, an altimeter or barometer, a low power Bluetooth module 3110, an NFC module 3112, a GPS module 3114, and a modem module 3116. The eLBS module 3120 may include hardware, software, or a combination of hardware and software suitable for performing any or all of the eLBS methods/operations discussed in this application. The eLBS module 3120 may be implemented as part of the operating system 3118, implemented by the operating system 3118, or execute via the operating system 3118. The eLBS module 3120 may also be implemented independent of the operating system 3118. The eLBS module 3120 may be configured to communicate with the operating system via system or function calls, Application Programming Interfaces, or via any other technique known in the art.

In this example illustrated in FIG. 31, the eLBS module 3120 receives/collects information from the compass 3102, magnetometer 3104, gyroscope 3106, altimeter or barometer, low power Bluetooth module 3110, NFC module 3112, GPS module 3114, and modem module 3116 via the operating system 3118. The eLBS module 3120 may use any all of this information to determine the location of the device, determine a more precise location of the device, and send the information to the software applications (e.g., App4—eLBS, etc.).

FIG. 32 illustrates an example mobile device 102 that includes an eLBS module 3120, a sensor hub 3202, and an operating system 3118 suitable for executing software applications (e.g., App1, App2, App3, App4—eLBS, etc.). In the example illustrated in FIG. 32, the sensor hub is coupled to a compass 3102, a magnetometer 3104, a gyroscope 3106, an altimeter or barometer, a low power Bluetooth module 3110, an NFC module 3112, a GPS module 3114, and a modem module 3116. The eLBS module 3120 is coupled to the sensor hub 3202, which is coupled to the operating system 3118. The eLBS module 3120 may be implemented as part of the sensor hub 3202 or independent of the sensor hub 3202.

In this example illustrated in FIG. 32, the eLBS module 3120 receives/collects information from the compass 3102, magnetometer 3104, gyroscope 3106, altimeter or barometer, low power Bluetooth module 3110, NFC module 3112, GPS module 3114, and modem module 3116 via the sensor hub 3202. The eLBS module 3120 may use any all of this information to determine the location of the device, determine a more precise location of the device, and send the information to the software applications (e.g., App4—eLBS, etc.) executing/operating on the operating system 3318 via the sensor hub 3202.

In the various embodiments, the eLBS module 3120 may be configured to perform an eLBS algorithm/procedure. The eLBS algorithm may be implemented as an application running in an Operating System (OS) of a mobile device. The eLBS algorithm may be such that the sensor and communication modules that are used for eLBS are under the control of the OS, and the eLBS algorithm utilizes the OS of the mobile device to collect information. For instance sensors may be controlled by the OS and the eLBS algorithm may use the OS of the mobile device to obtain the sensor data to perform particular functions like dead reckoning The eLBS algorithm may also use the OS of the mobile device to obtain GPS data from the GPS module, to establish communication with the other devices by instructing the OS to establish communication either by low power Bluetooth, LTE, TDD, FDD, WiFi, Near Field Communications, Ultrawideband, etc.

The eLBS algorithm may also be implemented via a sensor hub of a mobile device. The use of a sensor hub may improve battery life of the mobile device. The sensor hub may be configured to establish and control the sensors and peripheral devices, freeing the OS from having to control these devices. This in effect allows the OS to focus its operations on executing the software applications. When the applications require additional inputs or outputs with peripheral hardware devices, it is accomplished via the sensor hub. The eLBS algorithm can be installed or imbedded in the sensor hub or some similar device. Embedding the eLBS algorithm in the sensor hub may allow the mobile device to implement and use a more efficient power management scheme, and continuously improve the mobile device's position without increasing the power consumption requirements of the OS.

FIG. 33 illustrates various information flows and operations in a system 3300 configured to use network based location information to perform the eLBS trilateration operations in accordance with the various embodiments. In the example illustrated in FIG. 33, the system 3300 includes a GPS system/component 3302, a WiFi system/component 3304, a Beacon/RFID system/component 3306, a wireless/mobile device 102, an eNodeB component 3310, a network core (EPC) system/component 3312, and an Evolved Serving Mobile Location Center (E-SMLC) system/component 3314. In some embodiments, the eNodeB component 3310, EPC system/component 3312, and E-SMLC system/component 3314 may be the same as, or configured to perform similar operations as, the eNodeB 404, core network 406, 454, and E-SMLC 418 components discussed above with reference to FIG. 4A.

In operation block 3320, the mobile device 102, eNodeB 3310, EPC system/component 3312 and E-SMLC system/component 3314 may communicate and perform various administrative tasks, such as initiation and authentication, by exchanging data and control information. In operation 3322, the GPS system/component 3302 may send GPS data to the mobile device 102. In operation 3324, the WiFi system/component 3304 may send WiFi data to the mobile device 102. In operation 3322, the Beacon/RFID system/component 3306 may send Beacon/RFID data to the mobile device 102. In operation 3322, the eNodeB component 3310 may send “network provided location information” to the mobile device 102. In operation block 3330, the mobile device may receive and use the GPS data, WiFi data, Beacon/RFID data, and network provided location information to determine the location of the mobile device 102 (e.g., by performing any or all of the operations illustrated in FIGS. 22-30, etc.) and generate a final waypoint (or final location estimate value).

As discussed above (e.g., with reference to FIGS. 4A-B, etc.), the E-SMLC system/component 3332 may be configured to perform various operations, such as collecting and maintaining tracking information about the mobile device 102, providing location services (e.g., via LLP), forwarding external or network initiated location service requests, exchanging almanac and/or assistance data with the eNodeB component 3310, etc. The eNodeB component 3310 may communicate with the E-SMLC system/component 3332 and the mobile device 102 (e.g., via System Information Blocks) to send and receive location information. In the various embodiments, the E-SMLC system/component 3314, eNodeB component 3310 and mobile device 102 may be configured to communicate any or all of the location information discussed in this application, such as network provided location information, positional location information, updated position information, final waypoint, updated final waypoint, more precise location information, location estimation set, previously computed location information, initial position value, dead reckoning position estimate value/set, combined sensor position estimate value, trilateration position estimate value, final location estimate value, temporary position and error values, etc.)

Returning to FIG. 33, in operation 3332, the E-SMLC system/component 3314 may send a request for location information to the eNodeB component 3310. In operation 3334, the eNodeB component 3310 may send a response to the request for location information to the E-SMLC system/component 3314. The response may include the network provided location information, a time stamp, cell of origin (COO) information, time of arrival (TOA) information, observed time difference of arrival (OTDOA) information, advanced forward link trilateration (AFLT) information, and angle of arrival (AOA) information, etc.

In operation 3336, the E-SMLC system/component 3314 may send a request for updated location information to the mobile device 102. In operation 3338, the mobile device 102 may a communication message that includes location information (e.g., the final waypoint generated in operation block 3330) to the E-SMLC system/component. In operation block 3340, the E-SMLC system/component 3314 may use the location information included in the received communication message to update its location/position information, update its tracking information, provide location services and/or generate updated network derived location information. In operation 3342, the E-SMLC system/component 3314 may send the updated network derived location information to the mobile device 102.

In operation block 3320, the eNodeB 3310, the EPC system/component 3312 and/or the E-SMLC system/component 3314 may determine that a timer has expired (e.g., which may be set each time location information is received from the mobile device 102, etc.) or that another event has occurred that indicates there is a high probability that the mobile device has moved a significant distance. In response to determining that the timer has expired (or another event has occurred) the system may repeat operations 3330 through 3342 so as to synchronize their information and/or so that each component maintains or has access to updated and precise location information.

FIG. 34 illustrates a method 3400 of providing an enhanced location based service via a mobile device in accordance with the various embodiments. Method 3400 may be performed by a processor of a mobile device. In block 3402, the processor may receive location information from one or more of a sensor of the mobile device and an external system. For example, the processor may receive one or more of global positioning system (GPS) data, Loran C data, Cellular identifier (Cell ID) data, WiFi data, beacon data, and radio frequency identification (RFID) data in block 3402. In block 3404, the processor may generate a first waypoint based on the received location information.

In some embodiments, in block 3402 the processor may determine whether the location information has been reported, in which case generating the first waypoint may include generating a new waypoint and selecting the new waypoint as the first waypoint (i.e., in response to determining that the location information has been reported) and/or selecting as the first waypoint one of a plurality of previously generated waypoints (i.e., in response determining that the location information has not been reported). Alternatively or in addition, in block 3402, the processor may perform any or all of the operations discussed above and illustrated in FIGS. 23 through 27.

In block 3406, the processor may receive network provided location information from an eNodeB, E-SMLC or another network server, etc. In block 3408, the processor may generate a second waypoint based on the received network provided location information. In block 3410, the processor may retrieve dead reckoning location information. In block 3412, the processor may generate a third waypoint based on the received network provided location information.

In block 3414, the processor may apply the first, second and third waypoints to a kalman filter to generate precise location information (e.g., a final waypoint, a final location estimation set, etc.). In some embodiment, the precise location information may be a final location estimation set that includes position information, velocity information, and acceleration information. The position information may include latitude value, a longitude value and an altitude value. Similarly, velocity information may include latitude value, a longitude value, and an altitude value. The acceleration information may also include a latitude value, a longitude value, and an altitude value. In block 3416, the processor may use the precise location information to provide an enhanced location based service (e.g., an emergency location service, commercial location service, internal location service, lawful intercept location service, etc.).

In optional block 3418, the processor may send the precise location information to a network server (or eNodeB, E-SMLC, etc.) In optional block 3420, the processor may receive location information from the network server in response to sending the precise location information. In optional block 3422, the processor may generate more precise location information (e.g., updated final waypoint, updated final location estimation set, etc.) based on the received location information.

In an embodiment, the method 3400 may further include grouping the mobile device with a wireless transceiver in proximity to the mobile device to form a communication group, sending the precise location information to the wireless transceiver, receiving on the mobile device location information from the wireless transceiver in response to sending the precise location information, and generating the more precise location information based on the location information received from the wireless transceiver.

The foregoing method descriptions and the process flow diagrams are provided merely as illustrative examples and are not intended to require or imply that the blocks of the various embodiments must be performed in the order presented. As will be appreciated by one of skill in the art the order of blocks in the foregoing embodiments may be performed in any order. Words such as “thereafter,” “then,” “next,” etc. are not intended to limit the order of the blocks; these words are simply used to guide the reader through the description of the methods. Further, any reference to claim elements in the singular, for example, using the articles “a,” “an” or “the” is not to be construed as limiting the element to the singular.

The various illustrative logical blocks, modules, circuits, and algorithm blocks described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both. To clearly illustrate this interchangeability of hardware and software, various illustrative components, blocks, modules, circuits, and blocks have been described above generally in terms of their functionality. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the overall system. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.

The hardware used to implement the various illustrative logics, logical blocks, modules, and circuits described in connection with the embodiments disclosed herein may be implemented or performed with a general purpose processor, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination thereof designed to perform the functions described herein. A general-purpose processor may be a microprocessor, but, in the alternative, the processor may be any conventional processor, controller, microcontroller, or state machine A processor may also be implemented as a combination of computing devices, e.g., a combination of a DSP and a microprocessor, a plurality of microprocessors, one or more microprocessors in conjunction with a DSP core, or any other such configuration. Alternatively, some blocks or methods may be performed by circuitry that is specific to a given function.

In one or more exemplary aspects, the functions described may be implemented in hardware, software, firmware, or any combination thereof. If implemented in software, the functions may be stored as one or more instructions or code on a non-transitory computer-readable medium or non-transitory processor-readable medium. The steps of a method or algorithm disclosed herein may be embodied in a processor-executable software module which may reside on a non-transitory computer-readable or processor-readable storage medium. Non-transitory computer-readable or processor-readable storage media may be any storage media that may be accessed by a computer or a processor. By way of example but not limitation, such non-transitory computer-readable or processor-readable media may include RAM, ROM, EEPROM, FLASH memory, CD-ROM or other optical disk storage, magnetic disk storage or other magnetic storage devices, or any other medium that may be used to store desired program code in the form of instructions or data structures and that may be accessed by a computer. Disk and disc, as used herein, includes compact disc (CD), laser disc, optical disc, digital versatile disc (DVD), floppy disk, and blu-ray disc where disks usually reproduce data magnetically, while discs reproduce data optically with lasers. Combinations of the above are also included within the scope of non-transitory computer-readable and processor-readable media. Additionally, the operations of a method or algorithm may reside as one or any combination or set of codes and/or instructions on a non-transitory processor-readable medium and/or computer-readable medium, which may be incorporated into a computer program product.

The preceding description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the following claims and the principles and novel features disclosed herein. 

What is claimed is:
 1. A method of providing an enhanced location based service via a mobile device, comprising: receiving, via a processor of the mobile device, location information from one or more of a sensor of the mobile device and an external system; generating, via the processor, a first waypoint based on the received location information; receiving, via the processor, network provided location information; generating, via the processor, a second waypoint based on the received network provided location information; retrieving, via the processor, dead reckoning location information; generating, via the processor, a third waypoint based on the received network provided location information; applying, via the processor, the first, second and third waypoints to a kalman filter to generate precise location information; and using, via the processor, the precise location information to provide the enhanced location based service.
 2. The method of claim 1, wherein: receiving the location information from one or more of the sensor of the mobile device and the external system comprises receiving one or more of global positioning system (GPS) data, Loran C data; Cellular identifier (Cell ID) data, WiFi data, beacon data, and radio frequency identification (RFID) data; and receiving the network provided location information comprises receiving the network provided location information from one of a network server and an eNodeB.
 3. The method of claim 1, further comprising: sending the precise location information to a network server; receiving on the mobile device location information from the network server in response to sending the precise location information; and generating more precise location information based on the location information received from the network server.
 4. The method of claim 3, wherein: sending the precise location information to the network server comprises sending one of a final waypoint and a final location estimation set to the network server; and generating more precise location information based on the location information received from the network server comprises generating one of an updated final waypoint and an updated final location estimation set based on the location information received from the network server.
 5. The method of claim 1, further comprising: grouping the mobile device with a wireless transceiver in proximity to the mobile device to form a communication group; sending the precise location information to the wireless transceiver; receiving on the mobile device location information from the wireless transceiver in response to sending the precise location information; and generating more precise location information based on the location information received from the wireless transceiver.
 6. The method of claim 1, further comprising determining whether the location information from one or more of the sensor and the external system has been reported, wherein generating the first waypoint based on the received location information comprises: generating a new waypoint and selecting the new waypoint as the first waypoint in response to determining that the location information from one or more of the sensor and the external system has been reported; and selecting as the first waypoint one of a plurality of previously generated waypoints in response determining that the location information from one or more of the sensor and the external system has not been reported.
 7. The method of claim 6, wherein generating the first waypoint based on the received location information comprises further comprises: determining whether a memory of the mobile device stores previously generated waypoints; and generating the first waypoint to include a null value in response to determining that the memory does not store previously generated waypoints, and wherein selecting as the first waypoint one of the plurality of previously generated waypoints in response determining that the location information from one or more of the sensor and the external system has not been reported comprises: selecting as the first waypoint one of the plurality of previously generated waypoints in response determining that memory of the mobile device stores previously generated waypoints.
 8. The method of claim 1, further comprising determining whether the network provided location information has been reported, wherein generating the second waypoint based on the network provided location information comprises: generating a new waypoint and selecting the new waypoint as the second waypoint in response to determining that the network provided location information has been reported; and selecting as the second waypoint one of a plurality of previously generated waypoints in response determining that the network provided location information has not been reported.
 9. The method of claim 1, further comprising: determining whether the dead reckoning location information is available; and determining whether the dead reckoning location information has been reported in response to determining that the dead reckoning location information is available, wherein: generating the third waypoint based on the dead reckoning location information comprises: generating the third waypoint to include a null value in response to determining that the dead reckoning location information is available; generating a new waypoint and selecting the new waypoint as the third waypoint in response to determining that the dead reckoning location information has been reported; and selecting as the third waypoint one of a plurality of previously generated waypoints in response determining that the dead reckoning location information has not been reported.
 10. The method of claim 1, wherein generating a third waypoint based on the received network provided location information comprises: generating a location estimation set that includes a position value, a velocity value and an acceleration value based on the network provided location information.
 11. The method of claim 1, wherein generating a third waypoint based on the received network provided location information comprises: generating a location estimation set that includes latitude value, a longitude value, and an altitude value based on the network provided location information.
 12. The method of claim 1, wherein applying the first, second and third waypoints to the kalman filter to generate precise location information comprises: applying the first, second and third waypoints to the kalman filter to generate a final location estimation set comprising position information, velocity information, and acceleration information.
 13. The method of claim 12, wherein the position information, velocity information, and acceleration information each include a latitude value, a longitude value, and an altitude value.
 14. A non-transitory computer readable storage medium having stored thereon processor-executable software instructions configured to cause a processor of a mobile device to perform operations comprising: receiving location information from one or more of a sensor of the mobile device and an external system; generating a first waypoint based on the received location information; receiving network provided location information; generating a second waypoint based on the received network provided location information; retrieving dead reckoning location information; generating a third waypoint based on the received network provided location information; applying the first, second and third waypoints to a kalman filter to generate precise location information; and using the precise location information to provide an enhanced location based service.
 15. The non-transitory computer readable storage medium of claim 14, wherein the stored processor-executable software instructions are configured to cause a processor of a mobile device to perform operations such that: receiving the location information from one or more of the sensor of the mobile device and the external system comprises receiving one or more of global positioning system (GPS) data, Loran C data; Cellular identifier (Cell ID) data, WiFi data, beacon data, and radio frequency identification (RFID) data; and receiving the network provided location information comprises receiving the network provided location information from one of a network server and an eNodeB.
 16. The non-transitory computer readable storage medium of claim 14, wherein the stored processor-executable software instructions are configured to cause a processor of a mobile device to perform operations further comprising: sending the precise location information to a network server; receiving on the mobile device location information from the network server in response to sending the precise location information; and generating more precise location information based on the location information received from the network server.
 17. The non-transitory computer readable storage medium of claim 16, wherein the stored processor-executable software instructions are configured to cause a processor of a mobile device to perform operations such that: sending the precise location information to the network server comprises sending one of a final waypoint and a final location estimation set to the network server; and generating more precise location information based on the location information received from the network server comprises generating one of an updated final waypoint and an updated final location estimation set based on the location information received from the network server.
 18. The non-transitory computer readable storage medium of claim 14, wherein the stored processor-executable software instructions are configured to cause a processor of a mobile device to perform operations further comprising: grouping the mobile device with a wireless transceiver in proximity to the mobile device to form a communication group; sending the precise location information to the wireless transceiver; receiving on the mobile device location information from the wireless transceiver in response to sending the precise location information; and generating more precise location information based on the location information received from the wireless transceiver.
 19. The non-transitory computer readable storage medium of claim 14, wherein: the stored processor-executable software instructions are configured to cause a processor of a mobile device to perform operations further comprising determining whether the location information from one or more of the sensor and the external system has been reported; and the stored processor-executable software instructions are configured to cause a processor of a mobile device to perform operations such that generating the first waypoint based on the received location information comprises: generating a new waypoint and selecting the new waypoint as the first waypoint in response to determining that the location information from one or more of the sensor and the external system has been reported; and selecting as the first waypoint one of a plurality of previously generated waypoints in response determining that the location information from one or more of the sensor and the external system has not been reported.
 20. The non-transitory computer readable storage medium of claim 19, wherein: the stored processor-executable software instructions are configured to cause a processor of a mobile device to perform operations such that generating the first waypoint based on the received location information comprises further comprises: determining whether a memory of the mobile device stores previously generated waypoints; and generating the first waypoint to include a null value in response to determining that the memory does not store previously generated waypoints; and the stored processor-executable software instructions are configured to cause a processor of a mobile device to perform operations such that selecting as the first waypoint one of the plurality of previously generated waypoints in response determining that the location information from one or more of the sensor and the external system has not been reported comprises: selecting as the first waypoint one of the plurality of previously generated waypoints in response determining that memory of the mobile device stores previously generated waypoints.
 21. The non-transitory computer readable storage medium of claim 14, wherein: the stored processor-executable software instructions are configured to cause a processor of a mobile device to perform operations further comprising determining whether the network provided location information has been reported; and the stored processor-executable software instructions are configured to cause a processor of a mobile device to perform operations such that generating the second waypoint based on the network provided location information comprises: generating a new waypoint and selecting the new waypoint as the second waypoint in response to determining that the network provided location information has been reported; and selecting as the second waypoint one of a plurality of previously generated waypoints in response determining that the network provided location information has not been reported.
 22. The non-transitory computer readable storage medium of claim 14, wherein: the stored processor-executable software instructions are configured to cause a processor of a mobile device to perform operations further comprising: determining whether the dead reckoning location information is available; and determining whether the dead reckoning location information has been reported in response to determining that the dead reckoning location information is available; and the stored processor-executable software instructions are configured to cause a processor of a mobile device to perform operations such that generating the third waypoint based on the dead reckoning location information comprises: generating the third waypoint to include a null value in response to determining that the dead reckoning location information is available; generating a new waypoint and selecting the new waypoint as the third waypoint in response to determining that the dead reckoning location information has been reported; and selecting as the third waypoint one of a plurality of previously generated waypoints in response determining that the dead reckoning location information has not been reported.
 23. The non-transitory computer readable storage medium of claim 14, wherein the stored processor-executable software instructions are configured to cause a processor of a mobile device to perform operations such that generating a third waypoint based on the received network provided location information comprises: generating a location estimation set that includes a position value, a velocity value and an acceleration value based on the network provided location information.
 24. The non-transitory computer readable storage medium of claim 14, wherein the stored processor-executable software instructions are configured to cause a processor of a mobile device to perform operations such that generating a third waypoint based on the received network provided location information comprises: generating a location estimation set that includes latitude value, a longitude value, and an altitude value based on the network provided location information.
 25. The non-transitory computer readable storage medium of claim 14, wherein the stored processor-executable software instructions are configured to cause a processor of a mobile device to perform operations such that applying the first, second and third waypoints to the kalman filter to generate precise location information comprises: applying the first, second and third waypoints to the kalman filter to generate a final location estimation set comprising position information, velocity information, and acceleration information.
 26. The non-transitory computer readable storage medium of claim 25, wherein the stored processor-executable software instructions are configured to cause a processor of a mobile device to perform operations such that the position information, velocity information, and acceleration information each include a latitude value, a longitude value, and an altitude value.
 27. A mobile device, comprising: a memory; and a processor coupled to the memory, wherein the processor is configured with processor-executable instructions to perform operations comprising: receiving location information from one or more of a sensor of the mobile device and an external system; generating a first waypoint based on the received location information; receiving network provided location information; generating a second waypoint based on the received network provided location information; retrieving dead reckoning location information; generating a third waypoint based on the received network provided location information; applying the first, second and third waypoints to a kalman filter to generate precise location information; and using the precise location information to provide an enhanced location based service.
 28. The mobile device of claim 27, wherein the processor is configured with processor-executable instructions to perform operations such that: receiving the location information from one or more of the sensor of the mobile device and the external system comprises receiving one or more of global positioning system (GPS) data, Loran C data; Cellular identifier (Cell ID) data, WiFi data, beacon data, and radio frequency identification (RFID) data; and receiving the network provided location information comprises receiving the network provided location information from one of a network server and an eNodeB.
 29. The mobile device of claim 27, wherein the processor is configured with processor-executable instructions to perform operations further comprising: sending the precise location information to a network server; receiving on the mobile device location information from the network server in response to sending the precise location information; and generating more precise location information based on the location information received from the network server.
 30. The mobile device of claim 29, wherein the processor is configured with processor-executable instructions to perform operations such that: sending the precise location information to the network server comprises sending one of a final waypoint and a final location estimation set to the network server; and generating more precise location information based on the location information received from the network server comprises generating one of an updated final waypoint and an updated final location estimation set based on the location information received from the network server.
 31. The mobile device of claim 27, wherein the processor is configured with processor-executable instructions to perform operations further comprising: grouping the mobile device with a wireless transceiver in proximity to the mobile device to form a communication group; sending the precise location information to the wireless transceiver; receiving on the mobile device location information from the wireless transceiver in response to sending the precise location information; and generating more precise location information based on the location information received from the wireless transceiver.
 32. The mobile device of claim 27, wherein: the processor is configured with processor-executable instructions to perform operations further comprising determining whether the location information from one or more of the sensor and the external system has been reported; and the processor is configured with processor-executable instructions to perform operations such that generating the first waypoint based on the received location information comprises: generating a new waypoint and selecting the new waypoint as the first waypoint in response to determining that the location information from one or more of the sensor and the external system has been reported; and selecting as the first waypoint one of a plurality of previously generated waypoints in response determining that the location information from one or more of the sensor and the external system has not been reported.
 33. The mobile device of claim 32, wherein: the processor is configured with processor-executable instructions to perform operations such that generating the first waypoint based on the received location information comprises further comprises: determining whether a memory of the mobile device stores previously generated waypoints; and generating the first waypoint to include a null value in response to determining that the memory does not store previously generated waypoints; and the processor is configured with processor-executable instructions to perform operations such that selecting as the first waypoint one of the plurality of previously generated waypoints in response determining that the location information from one or more of the sensor and the external system has not been reported comprises: selecting as the first waypoint one of the plurality of previously generated waypoints in response determining that memory of the mobile device stores previously generated waypoints.
 34. The mobile device of claim 27, wherein: the processor is configured with processor-executable instructions to perform operations further comprising determining whether the network provided location information has been reported; and the processor is configured with processor-executable instructions to perform operations such that generating the second waypoint based on the network provided location information comprises: generating a new waypoint and selecting the new waypoint as the second waypoint in response to determining that the network provided location information has been reported; and selecting as the second waypoint one of a plurality of previously generated waypoints in response determining that the network provided location information has not been reported.
 35. The mobile device of claim 27, wherein: the processor is configured with processor-executable instructions to perform operations further comprising: determining whether the dead reckoning location information is available; and determining whether the dead reckoning location information has been reported in response to determining that the dead reckoning location information is available; and the processor is configured with processor-executable instructions to perform operations such that generating the third waypoint based on the dead reckoning location information comprises: generating the third waypoint to include a null value in response to determining that the dead reckoning location information is available; generating a new waypoint and selecting the new waypoint as the third waypoint in response to determining that the dead reckoning location information has been reported; and selecting as the third waypoint one of a plurality of previously generated waypoints in response determining that the dead reckoning location information has not been reported.
 36. The mobile device of claim 27, wherein the processor is configured with processor-executable instructions to perform operations such that generating a third waypoint based on the received network provided location information comprises: generating a location estimation set that includes a position value, a velocity value and an acceleration value based on the network provided location information.
 37. The mobile device of claim 27, wherein the processor is configured with processor-executable instructions to perform operations such that generating a third waypoint based on the received network provided location information comprises: generating a location estimation set that includes latitude value, a longitude value, and an altitude value based on the network provided location information.
 38. The mobile device of claim 27, wherein the processor is configured with processor-executable instructions to perform operations such that applying the first, second and third waypoints to the kalman filter to generate precise location information comprises: applying the first, second and third waypoints to the kalman filter to generate a final location estimation set comprising position information, velocity information, and acceleration information.
 39. The mobile device of claim 38, wherein the processor is configured with processor-executable instructions to perform operations such that the position information, velocity information, and acceleration information each include a latitude value, a longitude value, and an altitude value. 